stanley control algorithm add
This commit is contained in:
liuhaoyan
parent
e02964f617
commit
7cbfa74370
3
.gitignore
vendored
3
.gitignore
vendored
@ -11,3 +11,6 @@ log/
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logs/
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data/
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files/
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.csv
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.png
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.~lock.U_turn_0.32_lqr.csv#
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.~lock.U_turn_0.32_lqr.csv#
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@ -0,0 +1 @@
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,sweetai,sweetai-Redmi-Book-Pro-15-2022,21.09.2026 17:35,file:///home/sweetai/.config/libreoffice/4;
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1
.~lock.U_turn_0.32_pp.csv#
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.~lock.U_turn_0.32_pp.csv#
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,sweetai,sweetai-Redmi-Book-Pro-15-2022,21.09.2026 17:34,file:///home/sweetai/.config/libreoffice/4;
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9476
U_turn_0.32_lqr.csv
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9476
U_turn_0.32_lqr.csv
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File diff suppressed because it is too large
Load Diff
765
U_turn_0.32_pp.csv
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765
U_turn_0.32_pp.csv
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@ -0,0 +1,765 @@
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distance_m,current_x_m,current_y_m,lateral_error_m,heading_error_rad,heading_error_deg,yaw_command
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0.00000000,28.56646335,-1.19859079,0.02345768,-0.10932256,-6.26372133,-0.07003732
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65.79710831,1.28883219,-2.68764593,-0.00656031,0.00353883,0.20276026,0.00459039
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65.79710831,1.21311247,-2.69234249,-0.00862364,0.00297927,0.17069935,-0.00818863
|
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65.99574583,1.13732538,-2.69578673,-0.00563845,0.00354575,0.20315641,0.00728537
|
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|
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66.66243730,0.43030265,-2.73396913,-0.00660549,0.00343822,0.19699558,0.00407625
|
||||
66.66243730,0.35457578,-2.73851739,-0.00852446,0.00301384,0.17268011,-0.00726209
|
||||
66.82911017,0.25356327,-2.74380205,-0.00713401,0.00314902,0.18042554,0.00125394
|
||||
66.82911017,0.17782367,-2.74815218,-0.00884260,0.00291871,0.16722962,-0.01004657
|
||||
66.99578303,0.10203039,-2.75155679,-0.00644856,0.00351785,0.20155815,0.00481700
|
||||
67.16245590,0.00105344,-2.75749841,-0.00572070,0.00306033,0.17534388,0.00425309
|
||||
67.16245590,-0.07470240,-2.76157788,-0.00716859,0.00305866,0.17524826,-0.00151445
|
||||
67.16245590,-0.15045600,-2.76569159,-0.00864931,0.00303935,0.17414177,-0.01322948
|
||||
|
770
U_turn_0.32_stanley.csv
Normal file
770
U_turn_0.32_stanley.csv
Normal file
@ -0,0 +1,770 @@
|
||||
distance_m,current_x_m,current_y_m,lateral_error_m,heading_error_rad,heading_error_deg,yaw_command
|
||||
0.00000000,28.62685273,-1.20364789,0.00945646,-0.10377289,-5.94574871,-0.06253354
|
||||
0.19863752,28.52531592,-1.21029591,0.00035890,-0.09543849,-5.46822286,-0.05857789
|
||||
0.19863752,28.44912456,-1.21549381,0.00528971,-0.08972765,-5.14101582,-0.05777456
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61.22208163,5.56782221,-2.45844298,-0.00910915,0.00326751,0.18721475,-0.01435101
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61.42071915,5.46645516,-2.46331232,-0.00665196,0.00372933,0.21367490,0.00331229
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61.42071915,5.39049601,-2.46806918,-0.00878597,0.00312446,0.17901852,-0.01200983
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61.61935667,5.21319858,-2.47747590,-0.00825727,0.00316905,0.18157301,-0.00801107
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61.81799418,5.13721687,-2.48110098,-0.00541773,0.00357817,0.20501422,0.00943621
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61.81799418,5.03596100,-2.48641900,-0.00727151,0.00361608,0.20718622,-0.00044175
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61.81799418,4.96014692,-2.49079888,-0.00902463,0.00322984,0.18505641,-0.01390237
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62.41390674,4.45512213,-2.51733340,-0.00667064,0.00362967,0.20796487,0.00347671
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62.61254426,4.35415074,-2.52326390,-0.00530303,0.00319432,0.18302102,0.00952360
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62.61254426,4.27838562,-2.52702858,-0.00644089,0.00344824,0.19756932,0.00429678
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62.61254426,4.20267464,-2.53181259,-0.00858987,0.00283097,0.16220260,-0.01109388
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62.81118178,4.10162095,-2.53625353,-0.00572907,0.00371455,0.21282796,0.00879795
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62.81118178,4.02591713,-2.54128404,-0.00815031,0.00286149,0.16395113,-0.00778328
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63.00981930,3.92486730,-2.54579774,-0.00534304,0.00370215,0.21211762,0.01064990
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63.00981930,3.84914963,-2.55048674,-0.00744329,0.00312756,0.17919611,-0.00247115
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63.20845682,3.74813821,-2.55573356,-0.00538131,0.00332140,0.19030200,0.00943040
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63.20845682,3.67236054,-2.55931707,-0.00634868,0.00372027,0.21315581,0.00555680
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63.20845682,3.59665534,-2.56426064,-0.00866088,0.00294708,0.16885520,-0.01145901
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63.40709433,3.49558608,-2.56845647,-0.00554642,0.00403927,0.23143340,0.01073573
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63.40709433,3.41988820,-2.57361714,-0.00810671,0.00304006,0.17418275,-0.00711766
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63.60573185,3.31883339,-2.57822430,-0.00539010,0.00377511,0.21629770,0.01075624
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63.80436937,2.99060316,-2.59676083,-0.00877305,0.00290273,0.16631437,-0.01249093
|
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64.00300689,2.88956810,-2.60168915,-0.00642167,0.00333308,0.19097136,0.00427631
|
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64.00300689,2.81385344,-2.60652974,-0.00861525,0.00266408,0.15264033,-0.01160501
|
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64.20164441,2.73804655,-2.60968745,-0.00535597,0.00349568,0.20028773,0.01025102
|
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64.20164441,2.63707539,-2.61580446,-0.00798744,0.00285576,0.16362328,-0.00661623
|
||||
64.40028193,2.56130031,-2.61955399,-0.00532552,0.00316666,0.18143641,0.00954696
|
||||
64.40028193,2.48553197,-2.62342727,-0.00657302,0.00331663,0.19002879,0.00338898
|
||||
64.40028193,2.38453793,-2.62941097,-0.00903288,0.00284957,0.16326811,-0.01477164
|
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64.59891945,2.30870250,-2.63226378,-0.00546397,0.00392102,0.22465785,0.01102357
|
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64.59891945,2.23299264,-2.63745204,-0.00804499,0.00290536,0.16646464,-0.00692061
|
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64.79755696,2.13192400,-2.64211966,-0.00540127,0.00360202,0.20638026,0.01039162
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64.79755696,2.05617221,-2.64657775,-0.00726128,0.00322031,0.18451000,-0.00094103
|
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64.99619448,1.95514606,-2.65198246,-0.00536101,0.00326060,0.18681860,0.00969538
|
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64.99619448,1.87937249,-2.65586618,-0.00662757,0.00339745,0.19465966,0.00336424
|
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64.99619448,1.80364910,-2.66065048,-0.00876072,0.00277709,0.15911535,-0.01262917
|
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65.19483200,1.70260830,-2.66560067,-0.00644197,0.00320362,0.18355377,0.00390003
|
||||
65.19483200,1.62688631,-2.67039944,-0.00858825,0.00258182,0.14792750,-0.01153973
|
||||
65.39346952,1.52581445,-2.67476985,-0.00567496,0.00353693,0.20265098,0.00885812
|
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65.39346952,1.45010847,-2.67983886,-0.00812261,0.00265573,0.15216222,-0.00794375
|
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65.59210704,1.37432545,-2.68347472,-0.00536008,0.00307869,0.17639573,0.00917521
|
||||
65.59210704,1.27332130,-2.68900924,-0.00738668,0.00296067,0.16963384,-0.00236980
|
||||
65.59210704,1.19757925,-2.69328500,-0.00901106,0.00281237,0.16113709,-0.01463574
|
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65.79074456,1.09654054,-2.69808892,-0.00654607,0.00335144,0.19202320,0.00363122
|
||||
65.79074456,1.02082384,-2.70286690,-0.00867674,0.00273966,0.15697067,-0.01197515
|
||||
65.95741742,0.94503422,-2.70631527,-0.00633720,0.00330638,0.18944139,0.00465159
|
||||
65.95741742,0.84405581,-2.71224532,-0.00875261,0.00287913,0.16496199,-0.01235342
|
||||
66.12409029,0.76826349,-2.71555400,-0.00626474,0.00355266,0.20355256,0.00570549
|
||||
66.29076316,0.66729461,-2.72160132,-0.00564811,0.00300597,0.17222932,0.00753231
|
||||
66.29076316,0.59153365,-2.72550477,-0.00691477,0.00315426,0.18072607,0.00095275
|
||||
66.29076316,0.51580250,-2.72996675,-0.00873053,0.00283002,0.16214796,-0.01224730
|
||||
66.45743602,0.41476404,-2.73473816,-0.00684057,0.00341056,0.19541098,0.00202819
|
||||
66.45743602,0.33903583,-2.73926359,-0.00873015,0.00301360,0.17266645,-0.01194175
|
||||
66.62410889,0.23800997,-2.74428007,-0.00707638,0.00336765,0.19295211,0.00050832
|
||||
66.79078176,0.16227285,-2.74867819,-0.00566814,0.00308489,0.17675090,0.00761314
|
||||
66.79078176,0.06127201,-2.75417058,-0.00765291,0.00302147,0.17311724,-0.00399552
|
||||
66.95745462,-0.01448586,-2.75816030,-0.00583921,0.00311564,0.17851309,0.00411441
|
||||
66.95745462,-0.09026888,-2.76168272,-0.00673798,0.00358485,0.20539671,-0.00000858
|
||||
67.12412749,-0.19127627,-2.76706833,-0.00541019,0.00360678,0.20665347,-0.01498404
|
||||
67.12412749,-0.26711372,-2.76955433,-0.00533070,0.00495647,0.28398487,-0.01406899
|
||||
67.29080036,-0.34296248,-2.77178776,-0.00148456,0.00642871,0.36833771,-0.01190396
|
||||
|
4809
U_turn_0.64_lqr.csv
Normal file
4809
U_turn_0.64_lqr.csv
Normal file
File diff suppressed because it is too large
Load Diff
390
U_turn_0.64_pp.csv
Normal file
390
U_turn_0.64_pp.csv
Normal file
@ -0,0 +1,390 @@
|
||||
distance_m,current_x_m,current_y_m,lateral_error_m,heading_error_rad,heading_error_deg,yaw_command
|
||||
0.00000000,0.00080753,0.00328774,-0.00328773,-0.00001050,-0.00060185,0.09731814
|
||||
0.00000000,0.02337575,0.00372324,-0.00372296,-0.00001196,-0.00068528,0.10453524
|
||||
0.16666802,0.18245629,0.01144776,-0.00193220,0.01020628,0.58477679,0.09893948
|
||||
0.33333605,0.33393594,0.02692669,-0.00801091,0.01447804,0.82953034,0.04212047
|
||||
0.50000407,0.48543287,0.03841236,-0.01033076,0.02345470,1.34385546,0.01607652
|
||||
0.66667694,0.68751981,0.04911004,-0.01046412,0.02110459,1.20920383,0.01594301
|
||||
0.83334981,0.83901666,0.05728422,-0.00913699,0.01947751,1.11597933,0.01829453
|
||||
1.00002268,0.99046701,0.06573533,-0.00813655,0.01782923,1.02153937,0.01882410
|
||||
1.16669554,1.19237923,0.07761061,-0.00923660,0.01546016,0.88580217,0.01297300
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56.04931551,11.09363988,-2.16135235,-0.00593202,0.00305365,0.17496139,0.00458438
|
||||
56.04931551,10.94212018,-2.16948600,-0.00880162,0.00306033,0.17534388,-0.00884533
|
||||
56.24795303,10.74008004,-2.18007348,-0.00860853,0.00328993,0.18849883,-0.00716328
|
||||
56.44659055,10.58854655,-2.18796480,-0.00745175,0.00349115,0.20002819,0.00040546
|
||||
56.64522807,10.43703686,-2.19627034,-0.00670230,0.00331877,0.19015173,0.00305463
|
||||
56.84386559,10.28552647,-2.20460204,-0.00599637,0.00313066,0.17937369,0.00474137
|
||||
57.04250311,10.08349110,-2.21525729,-0.00584596,0.00327896,0.18787045,0.00556229
|
||||
57.04250311,9.93197057,-2.22344007,-0.00879352,0.00321602,0.18426411,-0.00911139
|
||||
57.24114063,9.72980752,-2.23412817,-0.00869262,0.00333618,0.19114894,-0.00821413
|
||||
57.43977814,9.57825658,-2.24212704,-0.00764275,0.00349330,0.20015113,-0.00048619
|
||||
57.63841566,9.42718237,-2.25051280,-0.00698278,0.00351428,0.20135324,0.00238268
|
||||
57.83705318,9.22576334,-2.26140818,-0.00710321,0.00379490,0.21743151,0.00228474
|
||||
58.03569070,9.07470365,-2.26952409,-0.00614784,0.00400971,0.22973951,0.00564627
|
||||
58.23432822,8.87324511,-2.28052282,-0.00636503,0.00418423,0.23973891,0.00505139
|
||||
58.23432822,8.72214472,-2.28854474,-0.00934207,0.00453113,0.25961476,-0.01056415
|
||||
58.43296574,8.57116650,-2.29642331,-0.00815148,0.00496815,0.28465423,-0.00038398
|
||||
58.63160326,8.37007034,-2.30766232,-0.00861208,0.00457309,0.26201898,-0.00412364
|
||||
58.83024077,8.21919964,-2.31612972,-0.00798880,0.00442337,0.25344027,-0.00091697
|
||||
59.02887829,8.01800064,-2.32731917,-0.00841541,0.00436615,0.25016177,-0.00319798
|
||||
59.22751581,7.86707502,-2.33559583,-0.00760665,0.00445985,0.25553030,0.00045524
|
||||
59.42615333,7.66574710,-2.34650219,-0.00773635,0.00474380,0.27179981,0.00029972
|
||||
59.62479085,7.51472834,-2.35486999,-0.00700146,0.00474237,0.27171785,0.00248047
|
||||
59.62479085,7.36373439,-2.36278969,-0.00995795,0.00514983,0.29506344,-0.01202089
|
||||
59.82342837,7.16174634,-2.37295676,-0.00935950,0.00542973,0.31110072,-0.00736921
|
||||
60.02206589,7.01020722,-2.38070102,-0.00795308,0.00547622,0.31376450,0.00136972
|
||||
60.22070341,6.80816859,-2.39129273,-0.00775460,0.00533222,0.30551363,0.00229232
|
||||
60.41934092,6.65665896,-2.39954715,-0.00686928,0.00496982,0.28474985,0.00489348
|
||||
60.61797844,6.50514884,-2.40784075,-0.00605710,0.00460743,0.26398608,0.00641880
|
||||
60.81661596,6.30311951,-2.41852626,-0.00596070,0.00449179,0.25736079,0.00679197
|
||||
60.81661596,6.15153090,-2.42639118,-0.00877793,0.00449990,0.25782525,-0.00596144
|
||||
61.01525348,5.94870101,-2.43744475,-0.00898095,0.00413655,0.23700683,-0.00868143
|
||||
61.21389100,5.79660068,-2.44527251,-0.00770720,0.00431608,0.24729310,0.00094989
|
||||
61.41252852,5.59387872,-2.45622151,-0.00783155,0.00410222,0.23503974,-0.00022575
|
||||
61.61116604,5.44187506,-2.46466720,-0.00716963,0.00376438,0.21568298,0.00219159
|
||||
61.80980356,5.28990634,-2.47323323,-0.00666271,0.00337313,0.19326630,0.00310509
|
||||
62.00844107,5.08733784,-2.48425774,-0.00687947,0.00324582,0.18597166,0.00207561
|
||||
62.20707859,4.93562345,-2.49240447,-0.00598039,0.00312637,0.17912781,0.00467380
|
||||
62.40571611,4.73361249,-2.50304779,-0.00581522,0.00331067,0.18968728,0.00557078
|
||||
62.40571611,4.58210903,-2.51125260,-0.00878960,0.00323485,0.18534328,-0.00881963
|
||||
62.60435363,4.43058768,-2.51905296,-0.00754949,0.00354336,0.20301981,0.00005971
|
||||
62.80299115,4.22858026,-2.52991873,-0.00762191,0.00350927,0.20106637,-0.00037867
|
||||
63.00162867,4.07708223,-2.53820379,-0.00684865,0.00337599,0.19343022,0.00271197
|
||||
63.20026619,3.87506718,-2.54900974,-0.00686084,0.00341605,0.19572517,0.00278353
|
||||
63.39890371,3.72355412,-2.55707224,-0.00586416,0.00345658,0.19804743,0.00602880
|
||||
63.39890371,3.57204698,-2.56527033,-0.00885224,0.00337838,0.19356683,-0.00965571
|
||||
63.59754122,3.37000786,-2.57586672,-0.00867204,0.00356172,0.20407166,-0.00788980
|
||||
63.79617874,3.21848044,-2.58387834,-0.00762269,0.00362538,0.20771898,0.00000686
|
||||
63.99481626,3.06696792,-2.59218702,-0.00686462,0.00344013,0.19710487,0.00310180
|
||||
64.19345378,2.86495064,-2.60318641,-0.00707006,0.00325321,0.18639513,0.00182969
|
||||
64.39209130,2.71345385,-2.61173613,-0.00658538,0.00291585,0.16706569,0.00281635
|
||||
64.59072882,2.56194131,-2.62002250,-0.00584714,0.00281809,0.16146494,0.00472058
|
||||
64.78936634,2.35990104,-2.63097062,-0.00600146,0.00278996,0.15985301,0.00419784
|
||||
64.78936634,2.20834905,-2.63904432,-0.00877599,0.00288652,0.16538547,-0.00931648
|
||||
64.98800385,2.00627292,-2.64967454,-0.00862516,0.00311588,0.17852675,-0.00787733
|
||||
65.18664137,1.85473212,-2.65772559,-0.00763466,0.00320481,0.18362207,-0.00105122
|
||||
65.38527889,1.70320678,-2.66594043,-0.00680326,0.00316046,0.18108124,0.00249981
|
||||
65.58391641,1.50116124,-2.67668762,-0.00675358,0.00325178,0.18631317,0.00292443
|
||||
65.78255393,1.34964340,-2.68499063,-0.00601713,0.00310563,0.17793935,0.00486461
|
||||
65.98119145,1.14761425,-2.69568874,-0.00591259,0.00323175,0.18516569,0.00550901
|
||||
66.14786431,0.99609001,-2.70363228,-0.00540924,0.00338172,0.19375807,0.00720445
|
||||
66.31453718,0.79409568,-2.71493190,-0.00655125,0.00298356,0.17094524,0.00304175
|
||||
66.48121005,0.64258195,-2.72298456,-0.00616706,0.00308274,0.17662796,0.00445533
|
||||
66.64788291,0.49107311,-2.73115931,-0.00591059,0.00306533,0.17563075,0.00510651
|
||||
66.81455578,0.28906161,-2.74216669,-0.00673536,0.00296377,0.16981143,0.00232080
|
||||
66.98122865,0.13753675,-2.75007708,-0.00620305,0.00318597,0.18254290,0.00456148
|
||||
67.14790151,-0.06449055,-2.76079313,-0.00673474,0.00330018,0.18908622,0.00110315
|
||||
67.31457438,-0.21600271,-2.76901016,-0.00651810,0.00322984,0.18505641,-0.03200774
|
||||
67.48124725,-0.41833518,-2.77380415,-0.00114644,0.00843333,0.48319417,-0.08131459
|
||||
|
378
U_turn_0.64_stanley.csv
Normal file
378
U_turn_0.64_stanley.csv
Normal file
@ -0,0 +1,378 @@
|
||||
distance_m,current_x_m,current_y_m,lateral_error_m,heading_error_rad,heading_error_deg,yaw_command
|
||||
0.00000000,0.00086575,0.00568208,-0.00568153,-0.00062532,-0.03582838,0.11606503
|
||||
0.00000000,0.01400480,0.00594550,-0.00593672,-0.00062676,-0.03591098,0.11639582
|
||||
0.16666802,0.15718998,0.01147780,-0.00205621,0.01129781,0.64731693,0.11841059
|
||||
0.33333605,0.30863471,0.02919346,-0.01065758,0.01357575,0.77783295,0.02543363
|
||||
0.50000407,0.51073027,0.04162719,-0.01305261,0.02356217,1.35001266,-0.00933137
|
||||
0.66667694,0.66243405,0.04618765,-0.00808037,0.02462528,1.41092461,0.02481285
|
||||
0.83334981,0.86444723,0.05685008,-0.00818893,0.02202818,1.26212200,0.02521115
|
||||
1.00002268,1.01584287,0.06676367,-0.00854462,0.01884243,1.07959192,0.01537508
|
||||
1.16669554,1.21777019,0.07839285,-0.00937890,0.01656232,0.94895132,0.00909126
|
||||
1.33336841,1.36922875,0.08694712,-0.00846047,0.01521045,0.87149463,0.01194387
|
||||
1.50004128,1.52065013,0.09623968,-0.00831093,0.01340620,0.76811875,0.00862815
|
||||
1.66671415,1.72259460,0.10779638,-0.00892227,0.01208302,0.69230600,0.00437460
|
||||
1.83338702,1.87408450,0.11632551,-0.00806165,0.01129073,0.64691139,0.00826078
|
||||
2.00005988,2.07600164,0.12888116,-0.00952357,0.00938989,0.53800104,-0.00460558
|
||||
2.16673275,2.22744199,0.13740023,-0.00870674,0.00903517,0.51767738,0.00032828
|
||||
2.50007849,2.42934019,0.14920020,-0.00537435,0.00822425,0.47121502,0.01526660
|
||||
2.66675135,2.58073598,0.15867985,-0.00564466,0.00717645,0.41118043,0.01143752
|
||||
2.66675135,2.73214334,0.16800183,-0.00948796,0.00640119,0.36676094,-0.01113807
|
||||
2.83342422,2.88362099,0.17607028,-0.00830962,0.00680649,0.38998337,-0.00771002
|
||||
3.16351959,3.08563863,0.18625214,-0.00348016,-0.00603493,-0.34577591,-0.01266456
|
||||
3.16351959,3.23735172,0.19272809,-0.00472363,-0.00454912,-0.26064529,-0.05612458
|
||||
3.49686531,3.43985775,0.19722608,0.00168158,0.00057527,0.03296075,0.05266435
|
||||
3.66353816,3.64174504,0.21361568,-0.00625205,-0.00495504,-0.28390272,-0.00603792
|
||||
3.83021102,3.79334510,0.22199356,-0.00761888,-0.00515411,-0.29530850,-0.01486909
|
||||
3.99688388,3.99561742,0.23016835,-0.00705003,-0.00295766,-0.16946162,-0.00917297
|
||||
4.16355674,4.14729117,0.23657681,-0.00639932,-0.00181319,-0.10388796,-0.00272189
|
||||
4.33022960,4.29891291,0.24373085,-0.00648043,-0.00144542,-0.08281672,-0.00259057
|
||||
4.49690245,4.50101417,0.25304765,-0.00718654,-0.00080609,-0.04618546,-0.00689258
|
||||
4.66357531,4.65259615,0.25959088,-0.00664938,-0.00003697,-0.00211804,-0.00188624
|
||||
4.86280145,4.85463628,0.26860495,-0.00657904,0.00064110,0.03673208,-0.00039805
|
||||
5.06202760,5.00617093,0.27541749,-0.00573569,0.00101009,0.05787397,0.00537879
|
||||
5.26125374,5.20817788,0.28545313,-0.00670581,0.00068663,0.03934121,-0.00127761
|
||||
5.46047989,5.35971911,0.29231726,-0.00589922,0.00101298,0.05803939,0.00391486
|
||||
5.65970603,5.56178452,0.30138420,-0.00584596,0.00144469,0.08277438,0.00467852
|
||||
5.65970603,5.71333145,0.30820847,-0.00847563,0.00169978,0.09739035,-0.01248992
|
||||
5.85893218,5.86486551,0.31464051,-0.00722789,0.00228841,0.13111611,-0.00227419
|
||||
6.05815832,6.06689356,0.32391812,-0.00743099,0.00241053,0.13811343,-0.00348338
|
||||
6.25738446,6.21842109,0.33082500,-0.00661183,0.00254466,0.14579803,0.00178638
|
||||
6.45661061,6.36995564,0.33765568,-0.00570284,0.00268786,0.15400322,0.00697526
|
||||
6.65583675,6.57200911,0.34675868,-0.00571755,0.00285205,0.16341021,0.00711267
|
||||
6.65583675,6.72354651,0.35375636,-0.00871575,0.00281612,0.16135155,-0.01222669
|
||||
6.85506290,6.92571272,0.36262295,-0.00853029,0.00314679,0.18029796,-0.01010183
|
||||
7.05428904,7.07741891,0.36921998,-0.00737301,0.00336785,0.19296337,-0.00130134
|
||||
7.25351519,7.22909195,0.37624048,-0.00662438,0.00321959,0.18446886,0.00287435
|
||||
7.45274133,7.43132452,0.38518292,-0.00648603,0.00337401,0.19331630,0.00409255
|
||||
7.65196747,7.58298181,0.39213362,-0.00567275,0.00325210,0.18633168,0.00818729
|
||||
7.85119362,7.78516867,0.40150914,-0.00598291,0.00302719,0.17344546,0.00601787
|
||||
7.85119362,7.93682668,0.40796555,-0.00849850,0.00336325,0.19270009,-0.00941564
|
||||
8.05041976,8.13895206,0.41718061,-0.00862525,0.00323798,0.18552241,-0.01073945
|
||||
8.24964591,8.29054984,0.42371873,-0.00741412,0.00350435,0.20078458,-0.00107043
|
||||
8.44887205,8.44214222,0.43099279,-0.00691056,0.00307410,0.17613314,0.00104380
|
||||
8.64809819,8.64450191,0.44023662,-0.00706931,0.00278867,0.15977922,-0.00044778
|
||||
8.84732434,8.79618839,0.44698387,-0.00607623,0.00269336,0.15431805,0.00533618
|
||||
9.04655048,8.99838781,0.45614023,-0.00616223,0.00243265,0.13938075,0.00450457
|
||||
9.24577663,9.15006019,0.46309476,-0.00541673,0.00211199,0.12100797,0.00807025
|
||||
9.44500277,9.35229907,0.47196641,-0.00520592,0.00210879,0.12082473,0.00944230
|
||||
9.44500277,9.50395253,0.47888676,-0.00799567,0.00185481,0.10627301,-0.00861031
|
||||
9.64422892,9.65576255,0.48559598,-0.00700646,0.00205697,0.11785551,-0.00101114
|
||||
9.84345506,9.85803060,0.49467517,-0.00699732,0.00188764,0.10815366,-0.00120474
|
||||
10.04268120,10.00976356,0.50158630,-0.00620992,0.00167921,0.09621171,0.00343979
|
||||
10.24190735,10.21213540,0.51053288,-0.00606944,0.00163602,0.09373705,0.00436611
|
||||
10.44113349,10.36389728,0.51746948,-0.00533107,0.00137631,0.07885695,0.00817980
|
||||
10.44113349,10.51564729,0.52455336,-0.00815970,0.00104813,0.06005322,-0.01179632
|
||||
10.64035964,10.71695247,0.53345798,-0.00801401,0.00115415,0.06612793,-0.01044171
|
||||
10.83958578,10.86789776,0.54014176,-0.00706816,0.00154264,0.08838666,-0.00223564
|
||||
11.03881193,11.06916974,0.54949542,-0.00736710,0.00161668,0.09262917,-0.00428573
|
||||
11.23803807,11.22010741,0.55636472,-0.00656772,0.00179174,0.10265941,0.00136490
|
||||
11.43726421,11.37103032,0.56316166,-0.00567950,0.00196787,0.11275091,0.00681287
|
||||
11.63649036,11.57221560,0.57249105,-0.00595519,0.00194890,0.11166352,0.00504717
|
||||
11.83571650,11.72323224,0.57906724,-0.00480393,0.00237137,0.13586928,0.01181098
|
||||
12.03494265,11.92533063,0.58919259,-0.00591809,0.00166225,0.09523990,0.00437445
|
||||
12.03494265,12.07692374,0.59584108,-0.00840781,0.00201145,0.11524734,-0.01131484
|
||||
12.23416879,12.27904687,0.60477252,-0.00827991,0.00235550,0.13496012,-0.00970917
|
||||
12.43339493,12.43063770,0.61157388,-0.00736198,0.00249770,0.14310747,-0.00289569
|
||||
12.63262108,12.63276071,0.62056053,-0.00727426,0.00276023,0.15814979,-0.00183230
|
||||
12.83184722,12.78434239,0.62749913,-0.00647328,0.00274537,0.15729783,0.00293279
|
||||
13.03107337,12.98645177,0.63672864,-0.00662551,0.00274180,0.15709346,0.00197170
|
||||
13.23029951,13.13803859,0.64353827,-0.00569046,0.00281242,0.16113981,0.00720608
|
||||
13.23029951,13.28962052,0.65045943,-0.00860510,0.00277737,0.15913174,-0.01143615
|
||||
13.42952566,13.49174988,0.65936987,-0.00845648,0.00301505,0.17274975,-0.00979944
|
||||
13.62875180,13.64335247,0.66585350,-0.00719780,0.00334668,0.19175058,-0.00010452
|
||||
13.82797794,13.79493069,0.67293229,-0.00651216,0.00313948,0.17987886,0.00348117
|
||||
14.02720409,13.99704113,0.68214284,-0.00664721,0.00308907,0.17699044,0.00255873
|
||||
14.22643023,14.14862934,0.68892503,-0.00567079,0.00313509,0.17962721,0.00801818
|
||||
14.42565638,14.35071599,0.69814466,-0.00582029,0.00304113,0.17424406,0.00702801
|
||||
14.42565638,14.50227309,0.70500894,-0.00871437,0.00302301,0.17320566,-0.01190992
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
47.30555088,17.57599298,-1.81191783,-0.00631907,0.00293683,0.16826781,0.00425791
|
||||
47.50418840,17.42468853,-1.82019886,-0.00558285,0.00280737,0.16085022,0.00788609
|
||||
47.50418840,17.27334563,-1.82796934,-0.00808073,0.00310920,0.17814426,-0.00668262
|
||||
47.70282591,17.07151537,-1.83908955,-0.00839568,0.00289034,0.16560403,-0.00959128
|
||||
47.90146343,16.92009234,-1.84705925,-0.00734281,0.00308632,0.17683286,-0.00158095
|
||||
48.10010095,16.71820303,-1.85776043,-0.00726032,0.00325965,0.18676396,-0.00063399
|
||||
48.29873847,16.56680237,-1.86597461,-0.00642946,0.00321220,0.18404554,0.00424592
|
||||
48.49737599,16.41541327,-1.87427292,-0.00569265,0.00307749,0.17632743,0.00787335
|
||||
48.69601351,16.21352835,-1.88486605,-0.00548425,0.00329374,0.18871739,0.00954266
|
||||
48.69601351,16.06215923,-1.89307995,-0.00846434,0.00318049,0.18222871,-0.00955777
|
||||
49.09328855,15.86035786,-1.90407593,-0.00487870,0.00312208,0.17888192,0.01201647
|
||||
49.09328855,15.70899873,-1.91291802,-0.00844323,0.00258254,0.14796848,-0.01046324
|
||||
49.29192606,15.55757835,-1.92080306,-0.00731628,0.00295256,0.16916939,-0.00192708
|
||||
49.49056358,15.35568315,-1.93144637,-0.00717214,0.00326632,0.18714645,-0.00038980
|
||||
49.68920110,15.20427916,-1.93979503,-0.00647894,0.00315307,0.18065777,0.00331929
|
||||
49.88783862,15.00236534,-1.95074510,-0.00663800,0.00316523,0.18135445,0.00235572
|
||||
50.08647614,14.85084005,-1.95880632,-0.00565000,0.00321315,0.18410018,0.00777009
|
||||
50.28511366,14.64881705,-1.96972472,-0.00578074,0.00312899,0.17927807,0.00695024
|
||||
50.28511366,14.49729962,-1.97788512,-0.00868600,0.00309967,0.17759784,-0.01138824
|
||||
50.48375118,14.34575531,-1.98554492,-0.00731490,0.00352405,0.20191332,-0.00069098
|
||||
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|
||||
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|
||||
51.07966373,13.79018502,-2.01546780,-0.00659127,0.00338505,0.19394932,0.00339017
|
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51.27830125,13.63866323,-2.02354643,-0.00561388,0.00339292,0.19440011,0.00872831
|
||||
51.27830125,13.48707514,-2.03183129,-0.00866711,0.00321888,0.18442803,-0.01108583
|
||||
51.47693877,13.28494169,-2.04252628,-0.00858019,0.00338267,0.19381271,-0.01003276
|
||||
51.67557629,13.13334578,-2.05028425,-0.00729758,0.00372623,0.21349732,0.00018407
|
||||
51.87421381,12.98180169,-2.05887583,-0.00681520,0.00331615,0.19000147,0.00205487
|
||||
52.07285133,12.77975895,-2.07046406,-0.00760473,0.00276469,0.15840501,-0.00420991
|
||||
52.27148884,12.62819896,-2.07854054,-0.00664678,0.00293087,0.16792630,0.00189998
|
||||
52.47012636,12.47664273,-2.08655020,-0.00560718,0.00309061,0.17707875,0.00777979
|
||||
52.66876388,12.27467707,-2.09748689,-0.00575576,0.00303649,0.17397784,0.00688234
|
||||
52.66876388,12.12323182,-2.10566749,-0.00867486,0.00308012,0.17647769,-0.01131096
|
||||
52.86740140,11.92127951,-2.11627961,-0.00851644,0.00337742,0.19351219,-0.00949795
|
||||
53.06603892,11.76981758,-2.12435266,-0.00753898,0.00348185,0.19949543,-0.00240131
|
||||
53.26467644,11.61836321,-2.13260921,-0.00673867,0.00343465,0.19679068,0.00226689
|
||||
53.46331396,11.41639224,-2.14320653,-0.00653992,0.00369548,0.21173513,0.00403068
|
||||
53.66195148,11.26492719,-2.15145150,-0.00572288,0.00359915,0.20621634,0.00809474
|
||||
53.86058899,11.06293307,-2.16244496,-0.00593466,0.00343488,0.19680434,0.00665698
|
||||
53.86058899,10.91139335,-2.17019215,-0.00849988,0.00371288,0.21273234,-0.00864018
|
||||
54.05922651,10.75986921,-2.17822787,-0.00746538,0.00373028,0.21372955,-0.00116380
|
||||
54.25786403,10.55783456,-2.18889593,-0.00734536,0.00382398,0.21909807,-0.00004464
|
||||
54.45650155,10.40631949,-2.19710166,-0.00647508,0.00369953,0.21196736,0.00491140
|
||||
54.65513907,10.20431518,-2.20833732,-0.00691726,0.00328730,0.18834856,0.00131630
|
||||
54.85377659,10.05282550,-2.21701377,-0.00656283,0.00283788,0.16259875,0.00228166
|
||||
55.05241411,9.90131718,-2.22539239,-0.00592640,0.00267600,0.15332335,0.00534151
|
||||
55.25105163,9.69911502,-2.23589801,-0.00560077,0.00301407,0.17269377,0.00775948
|
||||
55.25105163,9.54769361,-2.24413645,-0.00857359,0.00303649,0.17397784,-0.01061310
|
||||
55.44968914,9.34625869,-2.25519761,-0.00887294,0.00322078,0.18453732,-0.01254221
|
||||
55.64832666,9.19520915,-2.26296318,-0.00762071,0.00385879,0.22109249,-0.00241113
|
||||
55.84696418,8.99375993,-2.27387687,-0.00776866,0.00412820,0.23652872,-0.00296615
|
||||
56.04560170,8.84266886,-2.28216468,-0.00697781,0.00424265,0.24308570,0.00173625
|
||||
56.24423922,8.69158475,-2.29031132,-0.00602924,0.00444292,0.25456042,0.00691765
|
||||
56.44287674,8.49037980,-2.30096350,-0.00589773,0.00476144,0.27281068,0.00817342
|
||||
56.44287674,8.33954528,-2.30914278,-0.00909526,0.00477360,0.27350736,-0.01104801
|
||||
56.64151426,8.18864146,-2.31709781,-0.00796272,0.00512408,0.29358812,-0.00243318
|
||||
56.84015178,7.98742959,-2.32806656,-0.00817133,0.00513719,0.29433944,-0.00386724
|
||||
57.03878929,7.83649076,-2.33634483,-0.00733089,0.00512646,0.29372472,0.00094025
|
||||
57.23742681,7.68547854,-2.34470024,-0.00656820,0.00507950,0.29103363,0.00460027
|
||||
57.43606433,7.48414942,-2.35536574,-0.00644911,0.00546001,0.31283559,0.00589514
|
||||
57.43606433,7.33312289,-2.36358338,-0.00978583,0.00548624,0.31433823,-0.01497050
|
||||
57.83333937,7.13104976,-2.37330585,-0.00441102,0.00602292,0.34508774,0.01756851
|
||||
57.83333937,6.97956810,-2.38214824,-0.00838661,0.00504802,0.28923046,-0.00533654
|
||||
58.03197689,6.82805315,-2.39038171,-0.00748426,0.00475095,0.27220963,-0.00027104
|
||||
58.23061441,6.62603227,-2.40122879,-0.00754016,0.00451397,0.25863121,-0.00095346
|
||||
58.42925192,6.47451516,-2.40933597,-0.00654325,0.00439237,0.25166442,0.00451438
|
||||
58.62788944,6.27248422,-2.41999055,-0.00640921,0.00435136,0.24931483,0.00544381
|
||||
58.82652696,6.12078919,-2.42835059,-0.00569291,0.00396107,0.22695279,0.00831828
|
||||
58.82652696,5.96866659,-2.43657320,-0.00875715,0.00381087,0.21834675,-0.01046077
|
||||
59.02516448,5.76587793,-2.44716143,-0.00853891,0.00395416,0.22655664,-0.00845832
|
||||
59.22380200,5.61383109,-2.45521268,-0.00749048,0.00396322,0.22707574,-0.00112213
|
||||
59.42243952,5.41114214,-2.46600163,-0.00746550,0.00396036,0.22691181,-0.00086906
|
||||
59.62107704,5.25915613,-2.47415949,-0.00652575,0.00389289,0.22304592,0.00460306
|
||||
59.81971456,5.10723752,-2.48255817,-0.00584756,0.00360631,0.20662615,0.00753342
|
||||
60.01835207,4.90496918,-2.49334553,-0.00583954,0.00349926,0.20049264,0.00756110
|
||||
60.21698959,4.75344481,-2.50114723,-0.00455738,0.00373124,0.21378419,0.01450969
|
||||
60.41562711,4.55149848,-2.51314706,-0.00586635,0.00269149,0.15421128,0.00562437
|
||||
60.41562711,4.39998716,-2.52113265,-0.00854424,0.00289296,0.16575430,-0.01063820
|
||||
60.61426463,4.24847109,-2.52907301,-0.00745447,0.00311826,0.17866335,-0.00259409
|
||||
60.81290215,4.04644723,-2.53965691,-0.00724777,0.00340604,0.19515143,-0.00064744
|
||||
61.01153967,3.89494384,-2.54787111,-0.00640796,0.00334786,0.19181830,0.00409855
|
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61.21017719,3.69293291,-2.55879315,-0.00653867,0.00328563,0.18825294,0.00323063
|
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61.40881471,3.54143617,-2.56718914,-0.00590118,0.00306891,0.17583566,0.00612845
|
||||
61.40881471,3.38989881,-2.57497683,-0.00844870,0.00336932,0.19304773,-0.00899196
|
||||
61.60745222,3.18787859,-2.58597284,-0.00863825,0.00320362,0.18355377,-0.01081633
|
||||
61.80608974,3.03633548,-2.59366874,-0.00729859,0.00358747,0.20554698,-0.00040876
|
||||
62.00472726,2.83431140,-2.60456199,-0.00739606,0.00350164,0.20062924,-0.00120185
|
||||
62.20336478,2.68280096,-2.61288914,-0.00666460,0.00332116,0.19028834,0.00279785
|
||||
62.40200230,2.53129020,-2.62117952,-0.00591159,0.00316952,0.18160034,0.00656673
|
||||
62.60063982,2.32922335,-2.63174064,-0.00566529,0.00338386,0.19388102,0.00847687
|
||||
62.60063982,2.17768357,-2.64002109,-0.00871526,0.00322484,0.18476954,-0.01148128
|
||||
62.99791486,1.97560951,-2.65055661,-0.00462110,0.00347422,0.19905830,0.01391751
|
||||
62.99791486,1.82411687,-2.65945634,-0.00828392,0.00278591,0.15962079,-0.00889127
|
||||
63.19655237,1.67259482,-2.66772918,-0.00752888,0.00274275,0.15714826,-0.00372129
|
||||
63.39518989,1.52106789,-2.67588002,-0.00665123,0.00281285,0.16116441,0.00171662
|
||||
63.59382741,1.31902475,-2.68656376,-0.00653083,0.00303029,0.17362267,0.00283734
|
||||
63.79246493,1.16750575,-2.69469263,-0.00561955,0.00304578,0.17451060,0.00772923
|
||||
63.79246493,1.01599696,-2.70297163,-0.00862568,0.00293540,0.16818584,-0.01122561
|
||||
64.12581066,0.81395308,-2.71336206,-0.00564904,0.00336550,0.19282917,0.00836495
|
||||
64.29248353,0.66245782,-2.72175444,-0.00562410,0.00312041,0.17878630,0.00790500
|
||||
64.45915640,0.46046632,-2.73303486,-0.00673430,0.00279640,0.16022184,0.00112676
|
||||
64.62582926,0.30891288,-2.74042843,-0.00565999,0.00347184,0.19892170,0.00856049
|
||||
64.79250213,0.15740827,-2.74873205,-0.00553840,0.00329112,0.18856713,0.00875658
|
||||
64.95917500,-0.04458542,-2.76009122,-0.00673486,0.00287293,0.16460683,-0.00164955
|
||||
65.12584786,-0.19614655,-2.76735400,-0.00552686,0.00365494,0.20941287,-0.01606759
|
||||
65.29252073,-0.39830532,-2.77557142,-0.00354159,0.00581645,0.33325786,-0.08069245
|
||||
|
BIN
U_turn_heading_error_comparison.png
Normal file
BIN
U_turn_heading_error_comparison.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 112 KiB |
BIN
U_turn_lateral_error_comparison.png
Normal file
BIN
U_turn_lateral_error_comparison.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 216 KiB |
BIN
U_turn_yaw_command_comparison.png
Normal file
BIN
U_turn_yaw_command_comparison.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 144 KiB |
355
lqr_plot_path_tracking_error.py
Normal file
355
lqr_plot_path_tracking_error.py
Normal file
@ -0,0 +1,355 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
读取当前目录下：
|
||||
mission_center_logger.txt
|
||||
mission_center_logger1.txt
|
||||
mission_center_logger2.txt
|
||||
...
|
||||
|
||||
从 [path_follow] 日志中提取：
|
||||
LQR: dis=..., alpha=...
|
||||
path controller, current pose point:x:..., y:..., yaw:...
|
||||
|
||||
绘制：
|
||||
1. 横向误差 dis 随实际行驶路程变化
|
||||
2. 航向误差 alpha 随实际行驶路程变化
|
||||
|
||||
说明：
|
||||
- dis：按你日志中的 LQR dis，单位通常是 m
|
||||
- alpha：按你日志中的 alpha，单位是 rad
|
||||
- 路程：根据连续 current pose 的 x/y 计算累计行驶距离
|
||||
- 日志中同一时刻可能重复打印多次，因此脚本会保留数据，
|
||||
但累计距离对相同位置不会增加。
|
||||
"""
|
||||
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 1. 日志文件
|
||||
# ============================================================
|
||||
|
||||
LOG_PATTERN = "logs/mission_center_logger*.txt"
|
||||
|
||||
files = sorted(Path(".").glob(LOG_PATTERN))
|
||||
|
||||
if not files:
|
||||
raise FileNotFoundError(
|
||||
f"当前目录没有找到 {LOG_PATTERN}"
|
||||
)
|
||||
|
||||
print("找到日志文件：")
|
||||
for f in files:
|
||||
print(" ", f)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 2. 正则表达式
|
||||
# ============================================================
|
||||
|
||||
# LQR:
|
||||
# [LQR: dis=0.001038186, alpha=-0.003193076, ...
|
||||
lqr_pattern = re.compile(
|
||||
r"\[LQR:\s*"
|
||||
r"dis=([-+0-9.eE]+),\s*"
|
||||
r"alpha=([-+0-9.eE]+)"
|
||||
)
|
||||
|
||||
# current pose:
|
||||
# [path controller, current pose point:x:24.68, y:1.16, yaw:0.026;
|
||||
pose_pattern = re.compile(
|
||||
r"current pose point:x:([-+0-9.eE]+),\s*"
|
||||
r"y:([-+0-9.eE]+),\s*"
|
||||
r"yaw:([-+0-9.eE]+)"
|
||||
)
|
||||
|
||||
yaw_command_pattern = re.compile(
|
||||
r"PP output angular=([-+0-9.eE]+)"
|
||||
)
|
||||
|
||||
# ============================================================
|
||||
# 3. 读取日志
|
||||
# ============================================================
|
||||
|
||||
all_data = []
|
||||
|
||||
for file in files:
|
||||
print(f"\n读取：{file}")
|
||||
|
||||
pending_lqr = None
|
||||
pending_yaw = None
|
||||
|
||||
with file.open("r", encoding="utf-8", errors="ignore") as f:
|
||||
for line in f:
|
||||
|
||||
# ---- LQR误差 ----
|
||||
m_lqr = lqr_pattern.search(line)
|
||||
if m_lqr:
|
||||
dis = float(m_lqr.group(1))
|
||||
alpha = float(m_lqr.group(2))
|
||||
|
||||
pending_lqr = {
|
||||
"dis": dis,
|
||||
"alpha": alpha,
|
||||
}
|
||||
|
||||
# ---- 当前车辆位置 ----
|
||||
m_pose = pose_pattern.search(line)
|
||||
m_yaw = yaw_command_pattern.search(line)
|
||||
if m_yaw:
|
||||
pending_yaw = float(m_yaw.group(1))
|
||||
if m_pose and pending_lqr is not None:
|
||||
|
||||
x = float(m_pose.group(1))
|
||||
y = float(m_pose.group(2))
|
||||
yaw = float(m_pose.group(3))
|
||||
|
||||
|
||||
all_data.append({
|
||||
"file": file.name,
|
||||
"x": x,
|
||||
"y": y,
|
||||
"yaw": yaw,
|
||||
"dis": pending_lqr["dis"],
|
||||
"alpha": pending_lqr["alpha"],
|
||||
"yaw_command": pending_yaw,
|
||||
})
|
||||
|
||||
pending_lqr = None
|
||||
|
||||
|
||||
if not all_data:
|
||||
raise RuntimeError(
|
||||
"没有解析到数据，请检查日志格式是否和示例一致。"
|
||||
)
|
||||
|
||||
|
||||
print(f"\n总共解析 {len(all_data)} 个数据点")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 4. 根据 x/y 计算实际行驶路程
|
||||
# ============================================================
|
||||
|
||||
x = np.array([d["x"] for d in all_data])
|
||||
y = np.array([d["y"] for d in all_data])
|
||||
|
||||
dis = np.array([d["dis"] for d in all_data])
|
||||
alpha = np.array([d["alpha"] for d in all_data])
|
||||
yaw_command = np.array([d["yaw_command"] for d in all_data])
|
||||
|
||||
# 连续位置之间的距离
|
||||
step_distance = np.sqrt(
|
||||
np.diff(x) ** 2 +
|
||||
np.diff(y) ** 2
|
||||
)
|
||||
|
||||
# 累计路程
|
||||
distance = np.zeros(len(x))
|
||||
|
||||
if len(x) > 1:
|
||||
distance[1:] = np.cumsum(step_distance)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 5. 如果希望横向误差取绝对值，可以取消下面注释
|
||||
# ============================================================
|
||||
|
||||
# dis = np.abs(dis)
|
||||
|
||||
# 注意：
|
||||
# 不建议默认取绝对值。
|
||||
# 保留正负号可以观察车辆是在路径左侧还是右侧。
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 6. 输出基本统计信息
|
||||
# ============================================================
|
||||
|
||||
print("\n========== 误差统计 ==========")
|
||||
|
||||
print(
|
||||
f"总行驶距离 : {distance[-1]:.3f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"横向误差最大值 : {np.max(dis):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"横向误差最小值 : {np.min(dis):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"横向误差绝对最大: {np.max(np.abs(dis)):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"横向误差 RMS : {np.sqrt(np.mean(dis ** 2)):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"航向误差最大值 : {np.max(alpha):.6f} rad "
|
||||
f"({np.degrees(np.max(alpha)):.3f} deg)"
|
||||
)
|
||||
|
||||
print(
|
||||
f"航向误差最小值 : {np.min(alpha):.6f} rad "
|
||||
f"({np.degrees(np.min(alpha)):.3f} deg)"
|
||||
)
|
||||
|
||||
print(
|
||||
f"航向误差绝对最大: {np.max(np.abs(alpha)):.6f} rad "
|
||||
f"({np.degrees(np.max(np.abs(alpha))):.3f} deg)"
|
||||
)
|
||||
|
||||
print(
|
||||
f"航向误差 RMS : {np.sqrt(np.mean(alpha ** 2)):.6f} rad "
|
||||
f"({np.degrees(np.sqrt(np.mean(alpha ** 2))):.3f} deg)"
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 7. 转换航向误差为角度
|
||||
# ============================================================
|
||||
|
||||
alpha_deg = np.degrees(alpha)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 8. 图1：横向误差
|
||||
# ============================================================
|
||||
|
||||
plt.figure(figsize=(12, 5))
|
||||
|
||||
plt.plot(
|
||||
distance,
|
||||
dis,
|
||||
linewidth=2.0
|
||||
)
|
||||
|
||||
plt.axhline(
|
||||
0.0,
|
||||
linestyle="--",
|
||||
linewidth=0.8
|
||||
)
|
||||
|
||||
plt.xlabel("Travel Distance (m)")
|
||||
plt.ylabel("Lateral Error dis (m)")
|
||||
plt.title("Lateral Error vs Travel Distance")
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
plt.savefig(
|
||||
"lateral_error_vs_distance.png",
|
||||
dpi=200
|
||||
)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 9. 图2：航向误差
|
||||
# ============================================================
|
||||
|
||||
plt.figure(figsize=(12, 5))
|
||||
|
||||
plt.plot(
|
||||
distance,
|
||||
alpha_deg,
|
||||
linewidth=2.0
|
||||
)
|
||||
|
||||
plt.axhline(
|
||||
0.0,
|
||||
linestyle="--",
|
||||
linewidth=0.8
|
||||
)
|
||||
|
||||
plt.xlabel("Travel Distance (m)")
|
||||
plt.ylabel("Heading Error alpha (deg)")
|
||||
plt.title("Heading Error vs Travel Distance")
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
plt.savefig(
|
||||
"heading_error_vs_distance.png",
|
||||
dpi=200
|
||||
)
|
||||
|
||||
plt.show()
|
||||
|
||||
# ============================================================
|
||||
# 13. 航向误差 rad
|
||||
# ============================================================
|
||||
|
||||
plt.figure(
|
||||
figsize=(12, 5)
|
||||
)
|
||||
|
||||
plt.plot(
|
||||
distance,
|
||||
yaw_command,
|
||||
linewidth=1.2
|
||||
)
|
||||
|
||||
plt.axhline(
|
||||
0.0,
|
||||
linestyle="--",
|
||||
linewidth=0.8
|
||||
)
|
||||
|
||||
plt.xlabel(
|
||||
"Distance (m)"
|
||||
)
|
||||
|
||||
plt.ylabel(
|
||||
"Yaw Command (rad)"
|
||||
)
|
||||
|
||||
plt.title(
|
||||
"Recalculated Yaw Command"
|
||||
)
|
||||
|
||||
plt.grid(True)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
plt.show()
|
||||
# ============================================================
|
||||
# 10. 同时保存 CSV，方便后续 MATLAB / Excel 分析
|
||||
# ============================================================
|
||||
|
||||
output = np.column_stack(
|
||||
(
|
||||
distance,
|
||||
x,
|
||||
y,
|
||||
dis,
|
||||
alpha,
|
||||
alpha_deg,
|
||||
yaw_command,
|
||||
)
|
||||
)
|
||||
|
||||
np.savetxt(
|
||||
"path_tracking_error.csv",
|
||||
output,
|
||||
delimiter=",",
|
||||
header="distance_m,x_m,y_m,lateral_error_m,heading_error_rad,heading_error_deg,yaw_command",
|
||||
comments="",
|
||||
fmt="%.8f"
|
||||
)
|
||||
|
||||
print("\n输出文件：")
|
||||
print(" lateral_error_vs_distance.png")
|
||||
print(" heading_error_vs_distance.png")
|
||||
print(" path_tracking_error.csv")
|
||||
163
pp_lqr_stanley_error.py
Normal file
163
pp_lqr_stanley_error.py
Normal file
@ -0,0 +1,163 @@
|
||||
import csv
|
||||
import os
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
files = [
|
||||
"U_turn_0.32_lqr.csv",
|
||||
"U_turn_0.32_pp.csv",
|
||||
"U_turn_0.32_stanley.csv",
|
||||
]
|
||||
|
||||
data = {}
|
||||
|
||||
for file in files:
|
||||
if not os.path.exists(file):
|
||||
raise FileNotFoundError(f"找不到文件: {file}")
|
||||
|
||||
distance = []
|
||||
lateral_error = []
|
||||
heading_error = []
|
||||
yaw_command = []
|
||||
|
||||
with open(file, "r", encoding="utf-8-sig", newline="") as f:
|
||||
reader = csv.DictReader(f)
|
||||
|
||||
required = {"distance_m", "lateral_error_m", "heading_error_rad","yaw_command"}
|
||||
if not required.issubset(reader.fieldnames or []):
|
||||
raise ValueError(
|
||||
f"{file} 缺少必要列，需要: {required}，实际列: {reader.fieldnames}"
|
||||
)
|
||||
|
||||
for row in reader:
|
||||
try:
|
||||
distance.append(float(row["distance_m"]))
|
||||
lateral_error.append(abs(float(row["lateral_error_m"])))
|
||||
heading_error.append(abs(float(row["heading_error_rad"])))
|
||||
yaw_command.append(float(row["yaw_command"]))
|
||||
except (ValueError, TypeError):
|
||||
continue
|
||||
|
||||
name = os.path.splitext(os.path.basename(file))[0]
|
||||
data[name] = {
|
||||
"distance": distance,
|
||||
"lateral_error": lateral_error,
|
||||
"heading_error": heading_error,
|
||||
"yaw_command": yaw_command,
|
||||
}
|
||||
|
||||
# 颜色由 matplotlib 自动分配，不手动指定
|
||||
names = list(data.keys())
|
||||
|
||||
# =========================
|
||||
# 图1：Lateral Error
|
||||
# =========================
|
||||
plt.figure(figsize=(12, 6))
|
||||
|
||||
for name in names:
|
||||
plt.plot(
|
||||
data[name]["distance"],
|
||||
data[name]["lateral_error"],
|
||||
label=name,
|
||||
linewidth=1.5,
|
||||
)
|
||||
|
||||
|
||||
plt.xlabel("Distance (m)")
|
||||
plt.ylabel("Lateral Error (m)")
|
||||
plt.title("Lateral Error Comparison")
|
||||
plt.grid(True, alpha=0.3)
|
||||
plt.legend(["LQR", "Pure Pursuit", "Stanley"])
|
||||
plt.tight_layout()
|
||||
|
||||
lateral_output = "U_turn_lateral_error_comparison.png"
|
||||
plt.savefig(lateral_output, dpi=200)
|
||||
plt.show()
|
||||
|
||||
# =========================
|
||||
# 图2：Heading Error
|
||||
# =========================
|
||||
plt.figure(figsize=(12, 6))
|
||||
|
||||
for name in names:
|
||||
plt.plot(
|
||||
data[name]["distance"],
|
||||
data[name]["heading_error"],
|
||||
label=name,
|
||||
linewidth=1.5,
|
||||
)
|
||||
|
||||
plt.axhline(0, linewidth=2.0)
|
||||
plt.xlabel("Distance (m)")
|
||||
plt.ylabel("Heading Error (rad)")
|
||||
plt.title("Heading Error Comparison")
|
||||
plt.grid(True, alpha=0.3)
|
||||
plt.legend(["LQR", "Pure Pursuit", "Stanley"])
|
||||
plt.tight_layout()
|
||||
|
||||
heading_output = "U_turn_heading_error_comparison.png"
|
||||
plt.savefig(heading_output, dpi=200)
|
||||
plt.show()
|
||||
|
||||
|
||||
# =========================
|
||||
# 图3：Yaw Command
|
||||
# =========================
|
||||
plt.figure(figsize=(12, 6))
|
||||
|
||||
for name in names:
|
||||
plt.plot(
|
||||
data[name]["distance"],
|
||||
data[name]["yaw_command"],
|
||||
label=name,
|
||||
linewidth=1.5,
|
||||
)
|
||||
|
||||
plt.axhline(0, linewidth=2.0)
|
||||
plt.xlabel("Distance (m)")
|
||||
plt.ylabel("Yaw Command (rad)")
|
||||
plt.title("Yaw Command Comparison")
|
||||
plt.grid(True, alpha=0.3)
|
||||
plt.legend(["LQR", "Pure Pursuit", "Stanley"])
|
||||
plt.tight_layout()
|
||||
|
||||
yaw_output = "U_turn_yaw_command_comparison.png"
|
||||
plt.savefig(yaw_output, dpi=200)
|
||||
plt.show()
|
||||
print("读取完成：")
|
||||
for name in names:
|
||||
print(f"{name}: {len(data[name]['distance'])} points")
|
||||
|
||||
print("\n已保存：")
|
||||
print(lateral_output)
|
||||
print(heading_output)
|
||||
print(yaw_output)
|
||||
|
||||
|
||||
print("\n" + "=" * 100)
|
||||
print(
|
||||
f"{'Controller':<20}"
|
||||
f"{'Lat Max(m)':>12}"
|
||||
f"{'Lat Mean(m)':>13}"
|
||||
f"{'Lat RMSE(m)':>14}"
|
||||
f"{'Lat P95(m)':>13}"
|
||||
f"{'Head Max(deg)':>15}"
|
||||
f"{'Head RMSE(deg)':>16}"
|
||||
)
|
||||
print("=" * 100)
|
||||
|
||||
for name in names:
|
||||
lat = np.array(data[name]["lateral_error"])
|
||||
head = np.array(data[name]["heading_error"])
|
||||
|
||||
print(
|
||||
f"{name:<20}"
|
||||
f"{np.max(lat):>12.4f}"
|
||||
f"{np.mean(lat):>13.4f}"
|
||||
f"{np.sqrt(np.mean(lat**2)):>14.4f}"
|
||||
f"{np.percentile(lat, 95):>13.4f}"
|
||||
f"{np.degrees(np.max(head)):>15.4f}"
|
||||
f"{np.degrees(np.sqrt(np.mean(head**2))):>16.4f}"
|
||||
)
|
||||
|
||||
print("=" * 100)
|
||||
778
pp_stanley_plot_path_tracking_error.py
Normal file
778
pp_stanley_plot_path_tracking_error.py
Normal file
@ -0,0 +1,778 @@
|
||||
import re
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 参数
|
||||
# ============================================================
|
||||
|
||||
LOG_PATTERN = "logs/mission_center_logger*.txt"
|
||||
|
||||
# 每 5 个完整数据取 1 个
|
||||
SAMPLE_STEP = 40
|
||||
|
||||
CSV_FILE = "recalculated_tracking_error.csv"
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 1. 查找日志文件
|
||||
# ============================================================
|
||||
|
||||
files = sorted(
|
||||
Path(".").glob(LOG_PATTERN)
|
||||
)
|
||||
|
||||
if not files:
|
||||
|
||||
raise FileNotFoundError(
|
||||
f"当前目录没有找到：{LOG_PATTERN}"
|
||||
)
|
||||
|
||||
print("找到日志文件：")
|
||||
|
||||
for file in files:
|
||||
|
||||
print(" ", file)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 2. 正则表达式
|
||||
# ============================================================
|
||||
|
||||
# ------------------------------------------------------------
|
||||
# current pose
|
||||
#
|
||||
# [debug] [path_follow]
|
||||
# [current_pose_x:11.57,
|
||||
# current_pose_y:-2.13,
|
||||
# current_pose_yaw:-3.10]
|
||||
# ------------------------------------------------------------
|
||||
|
||||
current_pose_pattern = re.compile(
|
||||
r"current_pose_x:([-+0-9.eE]+),\s*"
|
||||
r"current_pose_y:([-+0-9.eE]+),\s*"
|
||||
r"current_pose_yaw:([-+0-9.eE]+)"
|
||||
)
|
||||
|
||||
|
||||
# ------------------------------------------------------------
|
||||
# current path
|
||||
#
|
||||
# [debug] [path_follow]
|
||||
# [current_path_x:11.59,
|
||||
# current_path_y:-2.12,
|
||||
# current_path_yaw:-3.10]
|
||||
# ------------------------------------------------------------
|
||||
|
||||
current_path_pattern = re.compile(
|
||||
r"current_path_x:([-+0-9.eE]+),\s*"
|
||||
r"current_path_y:([-+0-9.eE]+),\s*"
|
||||
r"current_path_yaw:([-+0-9.eE]+)"
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 3. 函数：角度归一化
|
||||
# ============================================================
|
||||
|
||||
def normalize_angle(angle):
|
||||
|
||||
return (
|
||||
angle + np.pi
|
||||
) % (
|
||||
2.0 * np.pi
|
||||
) - np.pi
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 4. 函数：根据 current pose + path 重新计算
|
||||
# ============================================================
|
||||
|
||||
def calculate_errors(
|
||||
current_x,
|
||||
current_y,
|
||||
current_yaw,
|
||||
path_x,
|
||||
path_y,
|
||||
path_yaw
|
||||
):
|
||||
|
||||
# --------------------------------------------------------
|
||||
# 世界坐标系下的位置差
|
||||
# --------------------------------------------------------
|
||||
|
||||
dx = path_x - current_x
|
||||
|
||||
dy = path_y - current_y
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
# 转换到车辆坐标系
|
||||
#
|
||||
# vehicle frame:
|
||||
#
|
||||
# x
|
||||
# ↑
|
||||
# |
|
||||
# |
|
||||
# ● -----> y
|
||||
#
|
||||
# 根据当前 yaw 进行旋转
|
||||
# --------------------------------------------------------
|
||||
|
||||
lon_dis = (
|
||||
np.cos(current_yaw) * dx
|
||||
+
|
||||
np.sin(current_yaw) * dy
|
||||
)
|
||||
|
||||
|
||||
dis = (
|
||||
-np.sin(current_yaw) * dx
|
||||
+
|
||||
np.cos(current_yaw) * dy
|
||||
)
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
# 车辆到路径点的欧氏距离
|
||||
# --------------------------------------------------------
|
||||
|
||||
distance = np.sqrt(
|
||||
dx * dx +
|
||||
dy * dy
|
||||
)
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
# 航向误差
|
||||
# --------------------------------------------------------
|
||||
|
||||
alpha = normalize_angle(
|
||||
path_yaw - current_yaw
|
||||
)
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
# deg
|
||||
# --------------------------------------------------------
|
||||
|
||||
alpha_deg = np.degrees(alpha)
|
||||
|
||||
|
||||
return (
|
||||
distance,
|
||||
lon_dis,
|
||||
dis,
|
||||
alpha,
|
||||
alpha_deg
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 5. 读取日志
|
||||
# ============================================================
|
||||
|
||||
all_data = []
|
||||
|
||||
total_raw_count = 0
|
||||
|
||||
|
||||
for file in files:
|
||||
|
||||
print()
|
||||
print("=" * 70)
|
||||
print(f"读取：{file}")
|
||||
print("=" * 70)
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
# 每个文件单独计数
|
||||
# --------------------------------------------------------
|
||||
|
||||
file_data_count = 0
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
# 临时保存 current pose
|
||||
# --------------------------------------------------------
|
||||
|
||||
pending_pose = None
|
||||
|
||||
|
||||
with file.open(
|
||||
"r",
|
||||
encoding="utf-8",
|
||||
errors="ignore"
|
||||
) as f:
|
||||
|
||||
|
||||
for line in f:
|
||||
|
||||
|
||||
# =================================================
|
||||
# current pose
|
||||
# =================================================
|
||||
|
||||
m_pose = current_pose_pattern.search(
|
||||
line
|
||||
)
|
||||
|
||||
|
||||
if m_pose:
|
||||
|
||||
pending_pose = {
|
||||
|
||||
"x": float(
|
||||
m_pose.group(1)
|
||||
),
|
||||
|
||||
"y": float(
|
||||
m_pose.group(2)
|
||||
),
|
||||
|
||||
"yaw": float(
|
||||
m_pose.group(3)
|
||||
),
|
||||
}
|
||||
|
||||
continue
|
||||
|
||||
|
||||
# =================================================
|
||||
# current path
|
||||
# =================================================
|
||||
|
||||
m_path = current_path_pattern.search(
|
||||
line
|
||||
)
|
||||
|
||||
|
||||
if (
|
||||
m_path is not None
|
||||
and
|
||||
pending_pose is not None
|
||||
):
|
||||
|
||||
|
||||
path_x = float(
|
||||
m_path.group(1)
|
||||
)
|
||||
|
||||
path_y = float(
|
||||
m_path.group(2)
|
||||
)
|
||||
|
||||
path_yaw = float(
|
||||
m_path.group(3)
|
||||
)
|
||||
|
||||
|
||||
# =================================================
|
||||
# 重新计算
|
||||
# =================================================
|
||||
|
||||
(
|
||||
point_distance,
|
||||
lon_dis,
|
||||
dis,
|
||||
alpha,
|
||||
alpha_deg
|
||||
) = calculate_errors(
|
||||
|
||||
pending_pose["x"],
|
||||
pending_pose["y"],
|
||||
pending_pose["yaw"],
|
||||
|
||||
path_x,
|
||||
path_y,
|
||||
path_yaw
|
||||
)
|
||||
|
||||
|
||||
# =================================================
|
||||
# 每 SAMPLE_STEP 个数据取一个
|
||||
# =================================================
|
||||
|
||||
if file_data_count % SAMPLE_STEP == 0:
|
||||
|
||||
all_data.append({
|
||||
|
||||
"file": file.name,
|
||||
|
||||
"current_x":
|
||||
pending_pose["x"],
|
||||
|
||||
"current_y":
|
||||
pending_pose["y"],
|
||||
|
||||
"current_yaw":
|
||||
pending_pose["yaw"],
|
||||
|
||||
"path_x":
|
||||
path_x,
|
||||
|
||||
"path_y":
|
||||
path_y,
|
||||
|
||||
"path_yaw":
|
||||
path_yaw,
|
||||
|
||||
"point_distance":
|
||||
point_distance,
|
||||
|
||||
"lon_dis":
|
||||
lon_dis,
|
||||
|
||||
"dis":
|
||||
dis,
|
||||
|
||||
"alpha":
|
||||
alpha,
|
||||
|
||||
"alpha_deg":
|
||||
alpha_deg,
|
||||
})
|
||||
|
||||
|
||||
file_data_count += 1
|
||||
|
||||
total_raw_count += 1
|
||||
|
||||
|
||||
# ------------------------------------------------
|
||||
# 清空
|
||||
# ------------------------------------------------
|
||||
|
||||
pending_pose = None
|
||||
|
||||
|
||||
print(
|
||||
f"原始完整数据：{file_data_count}"
|
||||
)
|
||||
|
||||
print(
|
||||
f"抽样后数据："
|
||||
f"{(file_data_count + SAMPLE_STEP - 1) // SAMPLE_STEP}"
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 6. 检查
|
||||
# ============================================================
|
||||
|
||||
if not all_data:
|
||||
|
||||
raise RuntimeError(
|
||||
"没有解析到 current_pose + current_path 数据。"
|
||||
)
|
||||
|
||||
|
||||
print()
|
||||
print("=" * 70)
|
||||
print("解析完成")
|
||||
print("=" * 70)
|
||||
|
||||
print(
|
||||
f"原始数据：{total_raw_count}"
|
||||
)
|
||||
|
||||
print(
|
||||
f"最终数据：{len(all_data)}"
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 7. 提取数据
|
||||
# ============================================================
|
||||
|
||||
current_x = np.array([
|
||||
d["current_x"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
current_y = np.array([
|
||||
d["current_y"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
current_yaw = np.array([
|
||||
d["current_yaw"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
|
||||
path_x = np.array([
|
||||
d["path_x"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
path_y = np.array([
|
||||
d["path_y"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
path_yaw = np.array([
|
||||
d["path_yaw"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
|
||||
point_distance = np.array([
|
||||
d["point_distance"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
lon_dis = np.array([
|
||||
d["lon_dis"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
dis = np.array([
|
||||
d["dis"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
alpha = np.array([
|
||||
d["alpha"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
alpha_deg = np.array([
|
||||
d["alpha_deg"]
|
||||
for d in all_data
|
||||
])
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 8. 计算车辆累计行驶距离
|
||||
# ============================================================
|
||||
|
||||
if len(path_x) > 1:
|
||||
|
||||
dx = np.diff(
|
||||
path_x
|
||||
)
|
||||
|
||||
dy = np.diff(
|
||||
path_y
|
||||
)
|
||||
|
||||
step_distance = np.sqrt(
|
||||
dx ** 2 +
|
||||
dy ** 2
|
||||
)
|
||||
|
||||
else:
|
||||
|
||||
step_distance = np.array([])
|
||||
for i in range(len(step_distance)
|
||||
):
|
||||
|
||||
if step_distance[i] > 0.5:
|
||||
step_distance[i] = 0.0
|
||||
print(
|
||||
f"Warning: step_distance[{i}] = {step_distance[i]:.3f} m, which is too large. Setting it to 0.0.")
|
||||
|
||||
|
||||
distance_m = np.zeros(
|
||||
len(path_x)
|
||||
)
|
||||
|
||||
|
||||
if len(path_x) > 1:
|
||||
|
||||
distance_m[1:] = np.cumsum(
|
||||
step_distance
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 9. 保存 CSV
|
||||
# ============================================================
|
||||
|
||||
print()
|
||||
print("=" * 70)
|
||||
print("保存 CSV")
|
||||
print("=" * 70)
|
||||
|
||||
|
||||
with open(
|
||||
CSV_FILE,
|
||||
"w",
|
||||
newline="",
|
||||
encoding="utf-8-sig"
|
||||
) as f:
|
||||
|
||||
writer = csv.writer(f)
|
||||
|
||||
|
||||
writer.writerow([
|
||||
|
||||
"distance_m",
|
||||
|
||||
"current_x_m",
|
||||
"current_y_m",
|
||||
|
||||
"lateral_error_m",
|
||||
|
||||
"heading_error_rad",
|
||||
|
||||
"heading_error_deg",
|
||||
|
||||
])
|
||||
|
||||
|
||||
for i in range(
|
||||
len(all_data)
|
||||
):
|
||||
|
||||
writer.writerow([
|
||||
|
||||
f"{distance_m[i]:.8f}",
|
||||
|
||||
f"{current_x[i]:.8f}",
|
||||
f"{current_y[i]:.8f}",
|
||||
|
||||
|
||||
f"{dis[i]:.8f}",
|
||||
|
||||
f"{alpha[i]:.8f}",
|
||||
|
||||
f"{alpha_deg[i]:.8f}",
|
||||
|
||||
])
|
||||
|
||||
|
||||
print(
|
||||
f"CSV 已保存："
|
||||
f"{Path(CSV_FILE).resolve()}"
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 11. 横向误差
|
||||
# ============================================================
|
||||
|
||||
plt.figure(
|
||||
figsize=(12, 5)
|
||||
)
|
||||
|
||||
plt.plot(
|
||||
distance_m,
|
||||
dis,
|
||||
linewidth=1.2
|
||||
)
|
||||
|
||||
plt.axhline(
|
||||
0.0,
|
||||
linestyle="--",
|
||||
linewidth=0.8
|
||||
)
|
||||
|
||||
plt.xlabel(
|
||||
"Distance (m)"
|
||||
)
|
||||
|
||||
plt.ylabel(
|
||||
"Lateral Error (m)"
|
||||
)
|
||||
|
||||
plt.title(
|
||||
"Recalculated Lateral Error"
|
||||
)
|
||||
|
||||
plt.grid(True)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 13. 航向误差 rad
|
||||
# ============================================================
|
||||
|
||||
plt.figure(
|
||||
figsize=(12, 5)
|
||||
)
|
||||
|
||||
plt.plot(
|
||||
distance_m,
|
||||
alpha,
|
||||
linewidth=1.2
|
||||
)
|
||||
|
||||
plt.axhline(
|
||||
0.0,
|
||||
linestyle="--",
|
||||
linewidth=0.8
|
||||
)
|
||||
|
||||
plt.xlabel(
|
||||
"Distance (m)"
|
||||
)
|
||||
|
||||
plt.ylabel(
|
||||
"Heading Error (rad)"
|
||||
)
|
||||
|
||||
plt.title(
|
||||
"Recalculated Heading Error"
|
||||
)
|
||||
|
||||
plt.grid(True)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 15. 车辆轨迹 + 路径点
|
||||
# ============================================================
|
||||
|
||||
plt.figure(
|
||||
figsize=(8, 8)
|
||||
)
|
||||
|
||||
plt.plot(
|
||||
current_x,
|
||||
current_y,
|
||||
linewidth=1.2,
|
||||
label="Current Pose"
|
||||
)
|
||||
|
||||
plt.plot(
|
||||
path_x,
|
||||
path_y,
|
||||
linestyle="--",
|
||||
linewidth=1.0,
|
||||
label="Current Path Point"
|
||||
)
|
||||
|
||||
|
||||
plt.scatter(
|
||||
current_x[0],
|
||||
current_y[0],
|
||||
s=50,
|
||||
label="Start"
|
||||
)
|
||||
|
||||
plt.scatter(
|
||||
current_x[-1],
|
||||
current_y[-1],
|
||||
s=50,
|
||||
label="End"
|
||||
)
|
||||
|
||||
|
||||
plt.xlabel(
|
||||
"X (m)"
|
||||
)
|
||||
|
||||
plt.ylabel(
|
||||
"Y (m)"
|
||||
)
|
||||
|
||||
plt.title(
|
||||
"Current Pose and Path"
|
||||
)
|
||||
|
||||
plt.axis(
|
||||
"equal"
|
||||
)
|
||||
|
||||
plt.grid(True)
|
||||
|
||||
plt.legend()
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 16. 误差统计
|
||||
# ============================================================
|
||||
|
||||
print()
|
||||
print("=" * 70)
|
||||
print("误差统计")
|
||||
print("=" * 70)
|
||||
|
||||
|
||||
print(
|
||||
f"最大横向误差："
|
||||
f"{np.max(np.abs(dis)):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"平均绝对横向误差："
|
||||
f"{np.mean(np.abs(dis)):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"横向误差 RMSE："
|
||||
f"{np.sqrt(np.mean(dis ** 2)):.6f} m"
|
||||
)
|
||||
|
||||
|
||||
print()
|
||||
|
||||
print(
|
||||
f"最大纵向误差："
|
||||
f"{np.max(np.abs(lon_dis)):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"平均绝对纵向误差："
|
||||
f"{np.mean(np.abs(lon_dis)):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"纵向误差 RMSE："
|
||||
f"{np.sqrt(np.mean(lon_dis ** 2)):.6f} m"
|
||||
)
|
||||
|
||||
|
||||
print()
|
||||
|
||||
print(
|
||||
f"最大航向误差："
|
||||
f"{np.max(np.abs(alpha_deg)):.6f} deg"
|
||||
)
|
||||
|
||||
print(
|
||||
f"平均绝对航向误差："
|
||||
f"{np.mean(np.abs(alpha_deg)):.6f} deg"
|
||||
)
|
||||
|
||||
print(
|
||||
f"航向误差 RMSE："
|
||||
f"{np.sqrt(np.mean(alpha_deg ** 2)):.6f} deg"
|
||||
)
|
||||
|
||||
|
||||
print()
|
||||
|
||||
print(
|
||||
f"最大车辆-路径点距离："
|
||||
f"{np.max(point_distance):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"平均车辆-路径点距离："
|
||||
f"{np.mean(point_distance):.6f} m"
|
||||
)
|
||||
|
||||
print(
|
||||
f"总行驶距离："
|
||||
f"{distance_m[-1]:.3f} m"
|
||||
)
|
||||
|
||||
@ -26,7 +26,7 @@ path_follow_handler_node:
|
||||
|
||||
device_id: "2072868304711761920"
|
||||
|
||||
controller_type: "lqr" # "pure_pursuit", "lqr", "pid"
|
||||
controller_type: "stanley" # "pure_pursuit", "lqr", "pid","stanley"
|
||||
|
||||
# logger
|
||||
logger_logger_name: "path_follow"
|
||||
|
||||
@ -188,6 +188,7 @@ protected:
|
||||
|
||||
logger_->debug("current_point_index_:{}, path_points_ size:{}, distance2:{}, distance1:{}",
|
||||
current_point_index_, path_points_.size(), distance2, distance1);
|
||||
|
||||
|
||||
// 如果current_point_index_+1的距离小于current_point_index_距离，current_point_index_++
|
||||
if (distance2 < distance1) {
|
||||
@ -216,7 +217,7 @@ protected:
|
||||
double mission_speed_{0.0};
|
||||
double turn_radius_{0.3};
|
||||
double max_allowed_speed_{1.0};
|
||||
double wheel_dis_{0.95};
|
||||
double wheel_dis_{0.5};
|
||||
std::vector<PathPoint> path_points_;
|
||||
int current_point_index_{0};
|
||||
bool is_initialized_{false};
|
||||
|
||||
@ -8,6 +8,7 @@
|
||||
#include "lqr_control.hpp"
|
||||
#include "pid_control.hpp"
|
||||
#include "pure_pursuit_control.hpp"
|
||||
#include "stanley_control.hpp"
|
||||
#include "spdlog/logger.h"
|
||||
#include "spdlog/spdlog.h"
|
||||
#include "spdlog/sinks/rotating_file_sink.h"
|
||||
@ -27,7 +28,7 @@ protected:
|
||||
// 从PathTrackingController的参数映射到原有控制器的init参数
|
||||
// 注意：原有控制器需要max_linear_vel, max_angular_vel, kp, ki, kd, look_ahead_dis, kv
|
||||
// 这里需要根据需求设置合适的参数值
|
||||
float max_linear_vel = mission_speed_;
|
||||
float max_linear_vel = mission_speed_ ;
|
||||
float max_angular_vel = mission_speed_ / turn_radius_; // 根据转弯半径估算最大角速度
|
||||
float max_angular_vel_theory = max_allowed_speed_/wheel_dis_;
|
||||
max_angular_vel = std::max(max_angular_vel, max_angular_vel_theory);
|
||||
@ -173,7 +174,7 @@ public:
|
||||
|
||||
protected:
|
||||
bool onInitialize() override {
|
||||
float max_linear_vel = mission_speed_;
|
||||
float max_linear_vel = mission_speed_ ;
|
||||
float max_angular_vel = mission_speed_ / turn_radius_;
|
||||
float max_angular_vel_theory = max_allowed_speed_/wheel_dis_;
|
||||
max_angular_vel = std::max(max_angular_vel, max_angular_vel_theory);
|
||||
@ -183,7 +184,7 @@ protected:
|
||||
float look_ahead_dis = 0.4; // 前视距离，需要根据实际情况调整
|
||||
float kv = 0.6; // 速度系数，需要根据实际情况调整
|
||||
adapter_look_ahead_dis_ = look_ahead_dis;
|
||||
logger_->debug("PP para mission_speed_={}, turn_radius_={} frontdis={}", mission_speed_, turn_radius_,adapter_look_ahead_dis_ );
|
||||
//logger_->debug("PP para mission_speed_={}, turn_radius_={} frontdis={}", mission_speed_, turn_radius_,adapter_look_ahead_dis_ );
|
||||
controller_->init(max_linear_vel, max_angular_vel, kp, ki, kd, adapter_look_ahead_dis_, kv);
|
||||
return true;
|
||||
}
|
||||
@ -258,40 +259,13 @@ private:
|
||||
dis = static_cast<float>(-dx * sin(path_yaw) + dy * cos(path_yaw));
|
||||
lon_dis = static_cast<float>(sqrt(dx * dx + dy * dy));
|
||||
kappa = static_cast<float>(path_points_[idx].kappa);
|
||||
logger_->info(
|
||||
"PurePursuit parameters:\n"
|
||||
" current_x:{}, current_y:{}, current_yaw:{}\n"
|
||||
" current_path_x:{}, current_path_y:{}, current_path_yaw:{}\n"
|
||||
" lookahead_x:{}, lookahead_y:{}, lookahead_yaw:{}, lookahead_kappa:{}\n"
|
||||
" dx:{}, dy:{}, target_yaw:{}, alpha:{}\n"
|
||||
" linear_val:{}, dis:{}, lon_dis:{}\n"
|
||||
" current_point_index:{}, idx:{}, path_size:{}",
|
||||
|
||||
current_pose.x,
|
||||
current_pose.y,
|
||||
current_pose.yaw,
|
||||
|
||||
path_points_[current_point_index_].x,
|
||||
path_points_[current_point_index_].y,
|
||||
path_points_[current_point_index_].yaw,
|
||||
|
||||
lookahead_point.x,
|
||||
lookahead_point.y,
|
||||
lookahead_point.yaw,
|
||||
lookahead_point.kappa,
|
||||
|
||||
dx,
|
||||
dy,
|
||||
target_yaw,
|
||||
alpha,
|
||||
|
||||
linear_val,
|
||||
dis,
|
||||
lon_dis,
|
||||
|
||||
current_point_index_,
|
||||
idx,
|
||||
path_points_.size()
|
||||
logger_->debug("current_pose_x:{}, current_pose_y:{},current_pose_yaw:{}",
|
||||
current_pose.x, current_pose.y,current_pose.yaw
|
||||
);
|
||||
logger_->debug("current_path_x:{}, current_path_y:{},current_path_yaw:{}",
|
||||
path_points_[idx].x,
|
||||
path_points_[idx].y,
|
||||
path_points_[idx].yaw
|
||||
);
|
||||
|
||||
}
|
||||
@ -335,6 +309,148 @@ private:
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace auto_drive_misson_center
|
||||
// Stanley控制器适配器
|
||||
class StanleyControllerAdapter : public PathTrackingController {
|
||||
public:
|
||||
StanleyControllerAdapter(std::shared_ptr<spdlog::logger> logger = nullptr)
|
||||
: logger_(logger), PathTrackingController(logger), controller_(std::make_shared<Stanley_Controller_C>(logger)) {}
|
||||
|
||||
protected:
|
||||
bool onInitialize() override {
|
||||
float max_linear_vel = mission_speed_ ;
|
||||
float max_angular_vel = mission_speed_ / turn_radius_;
|
||||
float max_angular_vel_theory = max_allowed_speed_/wheel_dis_;
|
||||
max_angular_vel = std::max(max_angular_vel, max_angular_vel_theory);
|
||||
float kp = 1.76;
|
||||
float ki = 0.0;
|
||||
float kd = 0.8;
|
||||
float look_ahead_dis = 0.4; // 前视距离，需要根据实际情况调整
|
||||
float kv = 0.6; // 速度系数，需要根据实际情况调整
|
||||
adapter_look_ahead_dis_ = look_ahead_dis;
|
||||
//logger_->debug("PP para mission_speed_={}, turn_radius_={} frontdis={}", mission_speed_, turn_radius_,adapter_look_ahead_dis_ );
|
||||
controller_->init(max_linear_vel, max_angular_vel, kp, ki, kd, adapter_look_ahead_dis_, kv);
|
||||
return true;
|
||||
}
|
||||
bool kappa_cal = false;
|
||||
ControlOutput onRun(const PathPoint& current_pose) override {
|
||||
float alpha = 0.0;
|
||||
float linear_val = 0.0;
|
||||
float dis = 0.0;
|
||||
float lon_dis = 0.0;
|
||||
float kappa = 0.0;
|
||||
if(!kappa_cal){
|
||||
computePathCurvature();
|
||||
kappa_cal = true;
|
||||
}
|
||||
|
||||
// Pure Pursuit使用前视点，计算需要特殊处理
|
||||
computeControlParamsWithLookAhead(current_pose, alpha, linear_val, dis, lon_dis, kappa);
|
||||
logger_->debug("Stanley Adapter alpha={}, linear_val={}, dis={}, lon_dis={},kappa={}", alpha, linear_val, dis,lon_dis,kappa);
|
||||
|
||||
float angular_velocity = controller_->update(alpha, linear_val, dis, lon_dis, kappa);
|
||||
//float angular_velocity = controller_->update(alpha, linear_val, dis);
|
||||
logger_->debug("Stanley output angular={}", angular_velocity);
|
||||
bool completed = isPathCompleted(current_pose);
|
||||
|
||||
return ControlOutput(mission_speed_, angular_velocity, completed);
|
||||
}
|
||||
|
||||
void onReset() override {
|
||||
controller_->reset();
|
||||
}
|
||||
|
||||
private:
|
||||
std::shared_ptr<Stanley_Controller_C> controller_;
|
||||
double adapter_look_ahead_dis_; //保存前视距离
|
||||
std::shared_ptr<spdlog::logger> logger_;
|
||||
|
||||
void computeControlParamsWithLookAhead(const PathPoint& current_pose,
|
||||
float& alpha, float& linear_val, float& dis, float& lon_dis, float& kappa) {
|
||||
// 查找前视点
|
||||
PathPoint lookahead_point;
|
||||
if (!findLookAheadPoint(current_pose, lookahead_point)) {
|
||||
// 如果找不到前视点，使用当前路径点
|
||||
int idx = current_point_index_;
|
||||
if (idx >= static_cast<int>(path_points_.size())) {
|
||||
idx = path_points_.size() - 1;
|
||||
}
|
||||
lookahead_point = path_points_[idx];
|
||||
}
|
||||
|
||||
// 计算从当前点到前视点的角度误差
|
||||
double dx = lookahead_point.x - current_pose.x;
|
||||
double dy = lookahead_point.y - current_pose.y;
|
||||
double target_yaw = atan2(dy, dx);
|
||||
|
||||
// 预瞄角度误差
|
||||
alpha = static_cast<float>(target_yaw - current_pose.yaw);
|
||||
while (alpha > M_PI) alpha -= 2 * M_PI;
|
||||
while (alpha < -M_PI) alpha += 2 * M_PI;
|
||||
|
||||
// 线速度
|
||||
linear_val = static_cast<float>(mission_speed_);
|
||||
|
||||
// 横向误差（用于更新索引）
|
||||
int idx = current_point_index_;
|
||||
if (idx >= static_cast<int>(path_points_.size()) - 1) {
|
||||
idx = path_points_.size() - 1;
|
||||
}
|
||||
double dx_path = current_pose.x - path_points_[idx].x;
|
||||
double dy_path = current_pose.y - path_points_[idx].y;
|
||||
double path_yaw = path_points_[idx].yaw;
|
||||
//dis = static_cast<float>(-dx_path * sin(path_yaw) + dy_path * cos(path_yaw));
|
||||
dis = static_cast<float>(-dx * sin(path_yaw) + dy * cos(path_yaw));
|
||||
lon_dis = static_cast<float>(sqrt(dx * dx + dy * dy));
|
||||
kappa = static_cast<float>(path_points_[idx].kappa);
|
||||
logger_->debug("current_pose_x:{}, current_pose_y:{},current_pose_yaw:{}",
|
||||
current_pose.x, current_pose.y,current_pose.yaw
|
||||
);
|
||||
logger_->debug("current_path_x:{}, current_path_y:{},current_path_yaw:{}",
|
||||
path_points_[idx].x,
|
||||
path_points_[idx].y,
|
||||
path_points_[idx].yaw
|
||||
);
|
||||
|
||||
}
|
||||
|
||||
bool findLookAheadPoint(const PathPoint& current_pose, PathPoint& lookahead_point) {
|
||||
if (path_points_.empty()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// 获取前视距离（从控制器参数获取）
|
||||
// 这里需要从controller_获取lookAheadDis，但由于接口限制，我们使用默认值
|
||||
double look_ahead_distance = adapter_look_ahead_dis_; // 需要与初始化时一致
|
||||
|
||||
// 从当前点开始沿路径向前查找
|
||||
int start_idx = current_point_index_;
|
||||
if (start_idx >= static_cast<int>(path_points_.size())) {
|
||||
start_idx = path_points_.size() - 1;
|
||||
}
|
||||
|
||||
double accumulated_dist = 0.0;
|
||||
for (int i = start_idx; i < static_cast<int>(path_points_.size()) - 1; ++i) {
|
||||
double dx = path_points_[i + 1].x - path_points_[i].x;
|
||||
double dy = path_points_[i + 1].y - path_points_[i].y;
|
||||
double segment_length = sqrt(dx * dx + dy * dy);
|
||||
|
||||
if (accumulated_dist + segment_length >= look_ahead_distance) {
|
||||
// 在前视距离内插值
|
||||
double ratio = (look_ahead_distance - accumulated_dist) / segment_length;
|
||||
lookahead_point.x = path_points_[i].x + ratio * dx;
|
||||
lookahead_point.y = path_points_[i].y + ratio * dy;
|
||||
lookahead_point.yaw = path_points_[i].yaw;
|
||||
lookahead_point.kappa = path_points_[i].kappa;
|
||||
return true;
|
||||
}
|
||||
accumulated_dist += segment_length;
|
||||
}
|
||||
|
||||
// 如果路径长度不够，使用最后一个点
|
||||
lookahead_point = path_points_.back();
|
||||
return true;
|
||||
}
|
||||
}; // namespace auto_drive_misson_center
|
||||
|
||||
}
|
||||
#endif // AUTO_DRIVE_MISSION_CENTER_PATH_TRACKING_CONTROLLER_ADAPTER_HPP_
|
||||
@ -41,13 +41,14 @@ public:
|
||||
{
|
||||
// std::cout<<"PurePursuit update!"<<std::endl;
|
||||
logger_->trace("PurePursuit update!");
|
||||
logger_->info("PurePursuit process! linear_val:{}, dis:{}, lon_dis:{}, kappa:{}", linear_val, dis, lon_dis, kappa);
|
||||
// logger_->info("PurePursuit process! linear_val:{}, dis:{}, lon_dis:{}, kappa:{}", linear_val, dis, lon_dis, kappa);
|
||||
float w_val = (2 * fabs(linear_val) * sin(alpha) / (lon_dis)) + dis;
|
||||
|
||||
//float w_val = linear_val*kappa;
|
||||
w_val = fabs(w_val) > maxAngularVel ? (maxAngularVel * (w_val > 0.0 ? 1.0 : -1.0)) : w_val;
|
||||
// std::cout << "linear_val: " << linear_val << ", w_val: " << w_val << std::endl;
|
||||
logger_->trace("linear_val:{}, w_val:{}", linear_val, w_val);
|
||||
|
||||
return w_val;
|
||||
};
|
||||
|
||||
|
||||
@ -0,0 +1,71 @@
|
||||
#ifndef _STANLEY_CONTROLL_H_
|
||||
#define _STANLEY_CONTROLL_H_
|
||||
|
||||
#include <iostream>
|
||||
#include <math.h>
|
||||
#include <Eigen/Dense>
|
||||
#include "base_following_controller.hpp"
|
||||
#include "pid_control.hpp"
|
||||
|
||||
using namespace Eigen;
|
||||
|
||||
class Stanley_Controller_C : public BaseFollowingController
|
||||
{
|
||||
public:
|
||||
Stanley_Controller_C() = delete;
|
||||
Stanley_Controller_C(std::shared_ptr<spdlog::logger> logger_recorder)
|
||||
:BaseFollowingController(logger_recorder), logger_(logger_recorder){};
|
||||
~Stanley_Controller_C(){};
|
||||
|
||||
void init(const float max_linear_vel, const float max_angular_vel, const float kp, const float ki, \
|
||||
const float kd, const float look_ahead_dis, const float kv)
|
||||
{
|
||||
// std::cout<<"PurePursuit init!"<<std::endl;
|
||||
logger_->info("PurePursuit init! max_linear_vel:{}, max_angular_vel:{}", max_linear_vel, max_angular_vel);
|
||||
maxLinearVel = max_linear_vel;
|
||||
maxAngularVel = max_angular_vel;
|
||||
lookAheadDis = look_ahead_dis;
|
||||
k_v = kv;
|
||||
}
|
||||
float update(const float alpha, const float linear_val, const float dis)
|
||||
{
|
||||
// std::cout<<"PurePursuit update!"<<std::endl;
|
||||
logger_->trace("PurePursuit update!");
|
||||
float w_val = 1.5 * alpha +1.0 * std::atan2(dis,linear_val);
|
||||
w_val = fabs(w_val) > maxAngularVel ? (maxAngularVel * (w_val > 0.0 ? 1.0 : -1.0)) : w_val;
|
||||
// std::cout << "linear_val: " << linear_val << ", w_val: " << w_val << std::endl;
|
||||
logger_->trace("linear_val:{}, w_val:{}", linear_val, w_val);
|
||||
return w_val;
|
||||
};
|
||||
float update(const float alpha, const float linear_val, const float dis, const float lon_dis,const float kappa)
|
||||
{
|
||||
// std::cout<<"PurePursuit update!"<<std::endl;
|
||||
logger_->trace("PurePursuit update!");
|
||||
// logger_->info("PurePursuit process! linear_val:{}, dis:{}, lon_dis:{}, kappa:{}", linear_val, dis, lon_dis, kappa);
|
||||
float w_val = 1.5 * alpha +1.0 * std::atan2(dis,linear_val);
|
||||
|
||||
//float w_val = linear_val*kappa;
|
||||
w_val = fabs(w_val) > maxAngularVel ? (maxAngularVel * (w_val > 0.0 ? 1.0 : -1.0)) : w_val;
|
||||
// std::cout << "linear_val: " << linear_val << ", w_val: " << w_val << std::endl;
|
||||
logger_->trace("linear_val:{}, w_val:{}", linear_val, w_val);
|
||||
|
||||
return w_val;
|
||||
};
|
||||
|
||||
void reset()
|
||||
{
|
||||
//pid_.reset();
|
||||
}
|
||||
|
||||
void test(){std::cout<<"pp test!"<<std::endl; logger_->debug("pp test!");};
|
||||
|
||||
private:
|
||||
float maxLinearVel;
|
||||
float maxAngularVel;
|
||||
float lookAheadDis, k_v;
|
||||
float maxSlowAngularVel, minToleranceAngle;
|
||||
//PID_Controller_C pid_;
|
||||
std::shared_ptr<spdlog::logger> logger_;
|
||||
};
|
||||
|
||||
#endif
|
||||
@ -9,7 +9,6 @@
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include <limits>
|
||||
|
||||
namespace auto_drive_misson_center {
|
||||
|
||||
constexpr double PathFollowHandler::POS_LOST_TIMEOUT_SECONDS;
|
||||
@ -406,7 +405,7 @@ void PathFollowHandler::processRunningState()
|
||||
ControlOutput output = path_controller_->run(current_pose_point);
|
||||
|
||||
auto twist_msg = geometry_msgs::msg::Twist();
|
||||
twist_msg.linear.x = output.linear_velocity;
|
||||
twist_msg.linear.x = output.linear_velocity * 2 ;
|
||||
twist_msg.angular.z = output.angular_velocity;
|
||||
cmd_vel_pub_->publish(twist_msg);
|
||||
std::map<std::string, double> property_updates;
|
||||
@ -693,7 +692,13 @@ bool PathFollowHandler::loadPathTrackingController(const std::string& controller
|
||||
LOG_INFO("Pure Pursuit path tracking controller loaded");
|
||||
reportEvent("info", "PATH_CONTROLLER_LOAD_OK", "Pure Pursuit控制器加载成功");
|
||||
return true;
|
||||
} else {
|
||||
} else if (controller_type == "stanley") {
|
||||
path_controller_ = std::make_shared<StanleyControllerAdapter>(logger);
|
||||
LOG_INFO("STAN path tracking controller loaded");
|
||||
reportEvent("info", "PATH_CONTROLLER_LOAD_OK", "STAN控制器加载成功");
|
||||
return true;
|
||||
}
|
||||
else {
|
||||
LOG_ERROR("load Path Tracking Controller ERROR");
|
||||
}
|
||||
|
||||
|
||||
@ -966,7 +966,7 @@ void PathVisualizer::publishOdomPath(){
|
||||
int main(int argc, char *argv[]) {
|
||||
rclcpp::init(argc, argv);
|
||||
std::string file_name;
|
||||
file_name = "1701895373952102400.json";
|
||||
file_name = "1699983696942006272.json";
|
||||
// std::cout << "请输入路径文件路径: ";
|
||||
// std::getline(std::cin, file_name);
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user