基于中心选择大逃杀优化算法的机器人逆运动学求解

doi:10.13229/j.cnki.jdxbgxb.20231190

Abstract

Abstract:

In order to solve the problems of high time complexity and insufficient global exploration ability of the battle royale optimization algorithm， this paper proposes an improved battle royale optimization algorithm based on chaos mapping， center selection and elite adaptive strategy. Compared with the test results of particle swarm optimization algorithm， whale optimization algorithm， and the battle royale optimization algorithm， the improved battle royale optimization algorithm significantly reduces time complexity and notably enhances convergence precision， speed， and stability. In the application of solving the inverse kinematics problem of robots， the accuracy and stability of the improved battle royale optimization algorithm are better than those of the traditional battle royale optimization algorithm， proving its practicality and potential for development in solving robot inverse kinematics problems.

Key words: computer application technology, battle royale optimization algorithm, center selection, elite adaptive strategy, inverse kinematics

CLC Number:

TP301.6

Yu-fei ZHANG,Li-min WANG,Jian-ping ZHAO,Zhi-yao JIA,Ming-yang LI. Robot inverse kinematics solution based on center selection battle royale optimization algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2703-2710.

Figures/Tables 9

Fig.1

Table 1

Table 2

Other relevant parameters of the all algorithm"

算法	参　数
PSO	惯性权重w=0.8，学习因子 $c 1 = c 2 = 2$
WOA	收敛因子a是从2到0的线性递减，螺旋形状的常数b= 1
BRO	伤害阈值为3
M-BRO	随机因子 $r 1 ∈ [0,1]$ ， $r 2 ∈ [0,1]$ ，移动步长 $λ d a m, d ∈ [0,1]$
TCABRO	$λ = 2$ ， $w 1 = w 2 = 1$ ， $r 1 ∈ [0,1]$ ， $r 2 ∈ [0,1]$ ，伤害阈值为3

Table 2

Table 3

Fig.2

Table 4

Fig.3

Table 5

Table 6

References 15

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函数类型	函数名称	最优值
单峰函数	F₁： Shifted and Rotated Bent Cigar Function	100
多峰函数	F₆： Shifted and Rotated Expanded Scaffer's F₆ Function	600
混合函数	F₁₃： Hybrid Function 3（N = 3）	1 300
混合函数	F₁₅： Hybrid Function 5（N = 4）	1 500
复合函数	F₂₃： Composition Function 3（N = 4）	2 300
复合函数	F₂₇： Composition Function 7（N = 6）	2 700

函数	PSO		WOA		BRO		M-BRO		TCABRO
函数	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差
F₁	1.34×10¹⁰	3.48×10⁹	5.09×10⁸	2.78×10⁸	2.17×10¹⁰	1.80×10⁹	4.76×10⁸	1.66×10⁸	4.98×10³	5.18×10³
F₆	5.83×10¹	8.94×10⁰	7.50×10¹	7.38×10⁰	7.10×10¹	6.25×10⁰	5.78×10¹	1.32×10¹	3.96×10¹	7.62×10⁰
F₁₃	4.82×10⁸	6.87×10⁸	4.94×10⁵	4.03×10⁵	1.49×10⁹	6.69×10⁸	8.91×10⁶	5.24×10⁶	1.29×10⁴	7.30×10⁴
F₁₅	4.73×10⁷	3.88×10⁷	2.94×10⁵	2.75×10⁵	1.76×10⁴	8.24×10³	2.80×10⁵	1.63×10⁵	2.32×10³	1.67×10³
F₂₃	8.52×10²	6.99×10¹	7.34×10²	8.86×10¹	1.05×10³	8.76×10¹	6.45×10²	6.58×10¹	6.08×10²	5.69×10¹
F₂₇	9.38×10²	1.06×10²	6.78×10²	8.76×10¹	1.40×10³	1.52×10²	6.06×10²	5.13×10¹	4.99×10²	5.21×10⁰

函数	PSO	WOA	BRO	M-BRO	TCABRO
F₁	0.94	1.03	3.26	3.17	0.84
F₆	0.62	0.71	3.61	3.39	1.24
F₁₃	0.41	0.49	3.40	3.20	0.97
F₁₅	0.37	0.47	3.34	3.16	0.93
F₂₃	0.87	0.97	3.93	3.73	1.64
F₂₇	1.16	1.28	4.19	4.08	2.01

序号	连杆长度/m	扭转角/（°）	偏移量/m	关节角/（°）
序号	连杆长度/m	扭转角/（°）	偏移量/m	最小值	最大值
1	0	90	0	-160	160
2	0	0	0	-245	45
3	0.431 8	-90	0.149 1	-45	225
4	0.020 3	90	0.133 1	-110	170
5	0	-90	0	-100	100
6	0	0	0	-266	266

算法	种群个数N	最大迭代次数T	平均值	标准差
TCABRO	25	100	3.84×10^-6	4.14×10^-6
	25	300	5.74×10^-7	7.24×10^-7
	50	100	9.74×10^-7	1.16×10^-6
	50	300	2.22×10^-8	3.14×10^-8
	100	100	7.62×10^-7	9.73×10^-7
	100	300	4.31×10^-9	9.15×10^-9
	200	100	1.65×10^-8	2.15×10^-8
	200	300	1.67×10^-9	2.28×10^-9
	300	100	1.55×10^-9	2.47×10^-9
	300	300	3.08×10^-10	4.29×10^-10
	300	500	2.10×10^-12	4.09×10^-12
BRO	25	100	3.70×10^-4	3.50×10^-4
	25	300	1.80×10^-4	2.10×10^-4
	50	100	1.90×10^-4	2.10×10^-4
	50	300	7.24×10^-5	1.50×10^-4
	100	100	1.91×10^-5	3.77×10^-5
	100	300	1.06×10^-4	2.13×10^-5
	200	100	6.95×10^-5	2.37×10^-5
	200	300	6.20×10^-6	1.53×10^-5
	300	100	1.88×10^-6	3.81×10^-6
	300	300	7.14×10^-7	2.78×10^-6
	300	500	1.89×10^-7	6.45×10^-7

Robot inverse kinematics solution based on center selection battle royale optimization algorithm

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