Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (3): 962-971.DOI: 10.11772/j.issn.1001-9081.2022010037
Special Issue: 前沿与综合应用
• Frontier and comprehensive applications • Previous Articles Next Articles
Xianbojun FAN1, Lijia CHEN1(), Shen LI2, Chenlu WANG1, Min WANG1, Zan WANG1, Mingguo LIU1
Received:
2022-01-13
Revised:
2022-03-14
Accepted:
2022-03-22
Online:
2022-04-14
Published:
2023-03-10
Contact:
Lijia CHEN
About author:
FAN Xianbojun, born in 1994, M. S. candidate. His research interests include swarm intelligence algorithm.Supported by:
范贤博俊1, 陈立家1(), 李珅2, 王晨露1, 王敏1, 王赞1, 刘名果1
通讯作者:
陈立家
作者简介:
范贤博俊(1994—),男,河南义马人,硕士研究生,主要研究方向:群智能算法基金资助:
CLC Number:
Xianbojun FAN, Lijia CHEN, Shen LI, Chenlu WANG, Min WANG, Zan WANG, Mingguo LIU. Robust joint modeling and optimization method for visual manipulators[J]. Journal of Computer Applications, 2023, 43(3): 962-971.
范贤博俊, 陈立家, 李珅, 王晨露, 王敏, 王赞, 刘名果. 鲁棒的视觉机械臂联合建模优化方法[J]. 《计算机应用》唯一官方网站, 2023, 43(3): 962-971.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022010037
编码类型 | 维度 | 配置 |
---|---|---|
Type 0 | 20 | |
Type 1 | 20 | |
Type 2 | 26 | |
Type 3 | 26 | |
Type 4 | 41 |
Tab.1 Five individual configurations
编码类型 | 维度 | 配置 |
---|---|---|
Type 0 | 20 | |
Type 1 | 20 | |
Type 2 | 26 | |
Type 3 | 26 | |
Type 4 | 41 |
编码类型 | 迭代次数 | 收敛时间/s | Fitness/mm |
---|---|---|---|
Type 0 | 2 137 | 39 | 1.086 935 |
Type 3 | 4 948 | 92 | 0.718 967 |
Type 4 | 7 679 | 145 | 0.726 831 |
Tab.2 Individual configuration under normal manipulator environment
编码类型 | 迭代次数 | 收敛时间/s | Fitness/mm |
---|---|---|---|
Type 0 | 2 137 | 39 | 1.086 935 |
Type 3 | 4 948 | 92 | 0.718 967 |
Type 4 | 7 679 | 145 | 0.726 831 |
编码类型 | 迭代次数 | 收敛时间/s | Fitness/mm |
---|---|---|---|
Type 1 | 1 944 | 37 | 1.886 338 |
Type 2 | 4 758 | 88 | 1.685 230 |
Type 3 | 4 988 | 93 | 1.458 970 |
Type 4 | 7 835 | 149 | 0.755 262 |
Tab.3 Individual configurations under broken manipulator environment
编码类型 | 迭代次数 | 收敛时间/s | Fitness/mm |
---|---|---|---|
Type 1 | 1 944 | 37 | 1.886 338 |
Type 2 | 4 758 | 88 | 1.685 230 |
Type 3 | 4 988 | 93 | 1.458 970 |
Type 4 | 7 835 | 149 | 0.755 262 |
趋平率阈值 | 切换率阈值 | 迭代次数 | Fitness/mm |
---|---|---|---|
0.95 | 0.50 | 14 194 | 0.740 816 |
0.98 | 0.50 | 39 418 | 0.750 376 |
0.90 | 0.50 | 7 835 | 0.755 262 |
0.55 | 6 547 | 0.812 778 | |
0.60 | 6 072 | 0.881 793 | |
0.85 | 0.50 | 5 768 | 0.898 836 |
0.55 | 5 373 | 0.916 278 | |
0.60 | 5 174 | 1.361 520 | |
0.75 | 0.50 | 3 726 | 1.232 095 |
0.55 | 3 169 | 1.391 849 | |
0.60 | 2 479 | 1.513 099 |
Tab.4 Different combinations of two parameters
趋平率阈值 | 切换率阈值 | 迭代次数 | Fitness/mm |
---|---|---|---|
0.95 | 0.50 | 14 194 | 0.740 816 |
0.98 | 0.50 | 39 418 | 0.750 376 |
0.90 | 0.50 | 7 835 | 0.755 262 |
0.55 | 6 547 | 0.812 778 | |
0.60 | 6 072 | 0.881 793 | |
0.85 | 0.50 | 5 768 | 0.898 836 |
0.55 | 5 373 | 0.916 278 | |
0.60 | 5 174 | 1.361 520 | |
0.75 | 0.50 | 3 726 | 1.232 095 |
0.55 | 3 169 | 1.391 849 | |
0.60 | 2 479 | 1.513 099 |
方案 | RSS | MAE | RMSE | SD |
---|---|---|---|---|
f1(x) | 54.932 | 0.609 | 0.741 | 0.422 |
f2(x) | 14.248 | 0.281 | 0.377 | 0.252 |
f3(x) | 11.727 | 0.265 | 0.342 | 0.217 |
Tab.5 Results of three schemes on different indexes
方案 | RSS | MAE | RMSE | SD |
---|---|---|---|---|
f1(x) | 54.932 | 0.609 | 0.741 | 0.422 |
f2(x) | 14.248 | 0.281 | 0.377 | 0.252 |
f3(x) | 11.727 | 0.265 | 0.342 | 0.217 |
采集点数 | 机械臂 | 迭代次数 | 不同算法下的Fitness/mm | ||||||
---|---|---|---|---|---|---|---|---|---|
AMECoDEs-LO | AMECoDEs | PSO | GA | DE | IMPEDE | DESPS | |||
25 | R1 | 5 000 | 0.565 326 | 0.982 446 | 0.830 997 | 2.561 803 | 1.786 521 | 1.177 729 | 1.355 992 |
10 000 | 0.532 048 | 0.719 843 | 0.770 335 | 2.057 273 | 1.237 504 | 0.969 473 | 0.765 316 | ||
20 000 | 0.532 048 | 0.520 238 | 0.770 335 | 2.005 239 | 1.031 816 | 0.901 608 | 0.591 952 | ||
R2 | 5 000 | 0.586 456 | 0.601 693 | 0.876 969 | 1.911 315 | 1.779 222 | 1.437 815 | 0.635 495 | |
10 000 | 0.585 898 | 0.573 826 | 0.741 698 | 1.852 563 | 1.092 410 | 1.135 834 | 0.599 992 | ||
20 000 | 0.585 463 | 0.568 397 | 0.718 350 | 1.787 653 | 1.086 331 | 0.872 544 | 0.599 821 | ||
50 | R1 | 5 000 | 0.726 315 | 1.148 595 | 1.870 231 | 3.041 135 | 1.593 779 | 0.744 047 | 2.842 765 |
10 000 | 0.716 056 | 0.980 794 | 1.683 181 | 2.551 561 | 1.517 333 | 0.668 578 | 2.753 945 | ||
20 000 | 0.677 934 | 0.744 180 | 1.644 061 | 2.550 204 | 1.329 803 | 0.665 560 | 0.933 366 | ||
R2 | 5 000 | 0.664 622 | 1.983 563 | 1.975 506 | 3.164 919 | 2.273 540 | 0.665 486 | 2.731 112 | |
10 000 | 0.656 657 | 1.433 057 | 1.700 505 | 2.823 198 | 2.117 706 | 0.659 641 | 1.134 591 | ||
20 000 | 0.650 878 | 0.767 033 | 1.698 549 | 2.432 036 | 1.367 968 | 0.630 471 | 0.942 106 | ||
75 | R1 | 5 000 | 0.689 986 | 0.733 063 | 1.823 818 | 5.371 781 | 1.091 436 | 0.826 387 | 1.079 996 |
10 000 | 0.689 852 | 0.733 061 | 1.800 903 | 3.579 188 | 1.086 935 | 0.794 721 | 1.042 488 | ||
20 000 | 0.689 852 | 0.733 061 | 1.710 061 | 2.822 724 | 1.086 787 | 0.786 419 | 0.947 404 | ||
R2 | 5 000 | 0.715 865 | 0.791 578 | 2.052 678 | 3.245 265 | 1.121 884 | 1.249 125 | 1.657 453 | |
10 000 | 0.690 582 | 0.707 336 | 1.832 811 | 3.087 309 | 1.121 884 | 1.161 167 | 1.158 240 | ||
20 000 | 0.689 395 | 0.707 065 | 1.690 035 | 2.948 902 | 1.121 884 | 0.799 485 | 0.983 625 | ||
100 | R1 | 5 000 | 0.774 702 | 1.172 915 | 3.391 321 | 2.591 554 | 1.719 753 | 1.893 676 | 2.185 051 |
10 000 | 0.755 262 | 0.798 311 | 2.135 919 | 2.423 959 | 1.594 458 | 1.797 107 | 1.613 516 | ||
20 000 | 0.755 262 | 0.798 311 | 1.484 260 | 2.423 633 | 1.511 633 | 1.162 780 | 1.336 374 | ||
R2 | 5 000 | 0.767 045 | 0.850 048 | 1.590 780 | 2.787 817 | 2.201 815 | 2.319 529 | 2.590 174 | |
10 000 | 0.738 244 | 0.806 822 | 1.470 112 | 2.363 048 | 1.686 311 | 1.276 485 | 1.880 933 | ||
20 000 | 0.738 244 | 0.806 822 | 1.467 441 | 2.360 980 | 1.576 970 | 1.159 064 | 1.347 907 |
Tab.6 Fitness comparison of two manipulators under seven algorithms in four collection areas
采集点数 | 机械臂 | 迭代次数 | 不同算法下的Fitness/mm | ||||||
---|---|---|---|---|---|---|---|---|---|
AMECoDEs-LO | AMECoDEs | PSO | GA | DE | IMPEDE | DESPS | |||
25 | R1 | 5 000 | 0.565 326 | 0.982 446 | 0.830 997 | 2.561 803 | 1.786 521 | 1.177 729 | 1.355 992 |
10 000 | 0.532 048 | 0.719 843 | 0.770 335 | 2.057 273 | 1.237 504 | 0.969 473 | 0.765 316 | ||
20 000 | 0.532 048 | 0.520 238 | 0.770 335 | 2.005 239 | 1.031 816 | 0.901 608 | 0.591 952 | ||
R2 | 5 000 | 0.586 456 | 0.601 693 | 0.876 969 | 1.911 315 | 1.779 222 | 1.437 815 | 0.635 495 | |
10 000 | 0.585 898 | 0.573 826 | 0.741 698 | 1.852 563 | 1.092 410 | 1.135 834 | 0.599 992 | ||
20 000 | 0.585 463 | 0.568 397 | 0.718 350 | 1.787 653 | 1.086 331 | 0.872 544 | 0.599 821 | ||
50 | R1 | 5 000 | 0.726 315 | 1.148 595 | 1.870 231 | 3.041 135 | 1.593 779 | 0.744 047 | 2.842 765 |
10 000 | 0.716 056 | 0.980 794 | 1.683 181 | 2.551 561 | 1.517 333 | 0.668 578 | 2.753 945 | ||
20 000 | 0.677 934 | 0.744 180 | 1.644 061 | 2.550 204 | 1.329 803 | 0.665 560 | 0.933 366 | ||
R2 | 5 000 | 0.664 622 | 1.983 563 | 1.975 506 | 3.164 919 | 2.273 540 | 0.665 486 | 2.731 112 | |
10 000 | 0.656 657 | 1.433 057 | 1.700 505 | 2.823 198 | 2.117 706 | 0.659 641 | 1.134 591 | ||
20 000 | 0.650 878 | 0.767 033 | 1.698 549 | 2.432 036 | 1.367 968 | 0.630 471 | 0.942 106 | ||
75 | R1 | 5 000 | 0.689 986 | 0.733 063 | 1.823 818 | 5.371 781 | 1.091 436 | 0.826 387 | 1.079 996 |
10 000 | 0.689 852 | 0.733 061 | 1.800 903 | 3.579 188 | 1.086 935 | 0.794 721 | 1.042 488 | ||
20 000 | 0.689 852 | 0.733 061 | 1.710 061 | 2.822 724 | 1.086 787 | 0.786 419 | 0.947 404 | ||
R2 | 5 000 | 0.715 865 | 0.791 578 | 2.052 678 | 3.245 265 | 1.121 884 | 1.249 125 | 1.657 453 | |
10 000 | 0.690 582 | 0.707 336 | 1.832 811 | 3.087 309 | 1.121 884 | 1.161 167 | 1.158 240 | ||
20 000 | 0.689 395 | 0.707 065 | 1.690 035 | 2.948 902 | 1.121 884 | 0.799 485 | 0.983 625 | ||
100 | R1 | 5 000 | 0.774 702 | 1.172 915 | 3.391 321 | 2.591 554 | 1.719 753 | 1.893 676 | 2.185 051 |
10 000 | 0.755 262 | 0.798 311 | 2.135 919 | 2.423 959 | 1.594 458 | 1.797 107 | 1.613 516 | ||
20 000 | 0.755 262 | 0.798 311 | 1.484 260 | 2.423 633 | 1.511 633 | 1.162 780 | 1.336 374 | ||
R2 | 5 000 | 0.767 045 | 0.850 048 | 1.590 780 | 2.787 817 | 2.201 815 | 2.319 529 | 2.590 174 | |
10 000 | 0.738 244 | 0.806 822 | 1.470 112 | 2.363 048 | 1.686 311 | 1.276 485 | 1.880 933 | ||
20 000 | 0.738 244 | 0.806 822 | 1.467 441 | 2.360 980 | 1.576 970 | 1.159 064 | 1.347 907 |
采集点数 | 系统平均误差 | 校验点平均误差 | ||
---|---|---|---|---|
R1 | R2 | R1 | R2 | |
25 | 0.532 048 | 0.585 463 | 1.345 582 | 1.317 775 |
50 | 0.677 934 | 0.650 878 | 0.901 385 | 0.913 546 |
75 | 0.689 852 | 0.689 395 | 0.820 336 | 0.811 455 |
100 | 0.755 262 | 0.738 244 | 0.761 395 | 0.750 017 |
Tab.7 Convergence and verification results of system
采集点数 | 系统平均误差 | 校验点平均误差 | ||
---|---|---|---|---|
R1 | R2 | R1 | R2 | |
25 | 0.532 048 | 0.585 463 | 1.345 582 | 1.317 775 |
50 | 0.677 934 | 0.650 878 | 0.901 385 | 0.913 546 |
75 | 0.689 852 | 0.689 395 | 0.820 336 | 0.811 455 |
100 | 0.755 262 | 0.738 244 | 0.761 395 | 0.750 017 |
评价 对象 | σ=0.0 | σ=0.5 | σ=1.0 | σ=1.5 | |||
---|---|---|---|---|---|---|---|
优化前 | 优化后 | 优化前 | 优化后 | 优化前 | 优化后 | ||
系统 整体 | 0.755 | 0.749 | 0.807 | 0.716 | 0.823 | 0.724 | 0.840 |
x轴 | 0.366 | 0.367 | 0.398 | 0.352 | 0.414 | 0.373 | 0.410 |
y轴 | 0.371 | 0.372 | 0.444 | 0.367 | 0.424 | 0.352 | 0.412 |
z轴 | 0.385 | 0.382 | 0.412 | 0.393 | 0.366 | 0.397 | 0.464 |
Tab.8 Fitness of system model after adding noise with different intensities
评价 对象 | σ=0.0 | σ=0.5 | σ=1.0 | σ=1.5 | |||
---|---|---|---|---|---|---|---|
优化前 | 优化后 | 优化前 | 优化后 | 优化前 | 优化后 | ||
系统 整体 | 0.755 | 0.749 | 0.807 | 0.716 | 0.823 | 0.724 | 0.840 |
x轴 | 0.366 | 0.367 | 0.398 | 0.352 | 0.414 | 0.373 | 0.410 |
y轴 | 0.371 | 0.372 | 0.444 | 0.367 | 0.424 | 0.352 | 0.412 |
z轴 | 0.385 | 0.382 | 0.412 | 0.393 | 0.366 | 0.397 | 0.464 |
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