面向移动机器人路径规划的增强型人工大猩猩算法

doi:10.16182/j.issn1004731x.joss.24-0163

摘要/Abstract

摘要：

为解决移动机器人在复杂地形场景的路径规划中易陷入局部最优和收敛速度慢等问题，提出了一种多策略集成的增强型人工大猩猩算法(enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite individual genetic strategies，QGGTO)。融合二次插值策略和精英个体遗传策略，促进候选解之间的信息交流以加速收敛，并维持种群遗传多样性以避免局部最优。针对包含规则障碍物和不规则障碍物的复杂地形场景，构建了综合考虑行走距离、安全性和转向角度的成本函数，用于统一评估算法的路径规划性能。实验结果表明：QGGTO整体寻优性能优于GTO等7种竞争算法。在4种复杂障碍环境下，QGGTO能够辅助机器人规划出最接近全局最优的路径，验证了其在实际应用中的有效性。

关键词: 机器人路径规划, 人工大猩猩算法, 二次插值, 精英个体遗传策略, 元启发式算法

Abstract:

To address the issues of susceptibility to local optima and slow convergence in mobile robot path planning within complex terrain scenarios, an enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite individual genetic strategies (QGGTO) is proposed. The algorithm integrates quadratic interpolation and elite individual genetic strategies to promote information exchange among candidate solutions, thereby accelerating convergence, while maintaining population diversity to avoid local optima. For complex terrains containing both regular and irregular obstacles, a cost function that comprehensively considers walking distance, safety, and turning angles is constructed to uniformly evaluate the path planning performance of the algorithm. Simulation experiments demonstrate that QGGTO overall optimization performance surpassing GTO and six other competitive algorithms. QGGTO can assist robots in planning paths closest to the global optimum in four complex obstacle environments, validating its effectiveness in practical applications.

Key words: robot path planning, artificial gorilla troops optimizer, quadratic interpolation, elite individual genetic strategy, metaheuristic algorithm

中图分类号:

TP391.9

叶晨,邵鹏,张少平等 . 面向移动机器人路径规划的增强型人工大猩猩算法[J]. 系统仿真学报, 2025, 37(6): 1474-1485.

Ye Chen,Shao Peng,Zhang Shaoping,et al . Enhanced Artificial Gorilla Algorithm for Mobile Robot Path Planning[J]. Journal of System Simulation, 2025, 37(6): 1474-1485.

图/表 6

图1

表1

CEC2017测试集的实验结果

函数	性能指标	MPSO^[31]	BWO^[32]	DBO^[33]	SCSO^[34]	GTO^[16]	EAGT^[29]	EGTO^[30]	QGGTO
$F 1$	平均值	1.97E+11	2.93E+11	3.99E+10	9.13E+10	1.31E+09	3.68E+08	5.54E+09	2.98E+03
$F 1$	标准差	2.72E+10	7.65E+09	5.30E+10	1.09E+10	1.27E+09	4.21E+08	3.64E+09	2.34E+03
$F 2$	平均值	9.54E+05	4.95E+05	6.75E+05	3.32E+05	2.50E+05	2.56E+05	2.24E+05	1.82E+05
$F 2$	标准差	2.03E+05	1.02E+05	3.17E+05	2.10E+04	2.19E+04	4.20E+04	2.94E+04	1.86E+04
$F 3$	平均值	2.55E+04	5.67E+04	2.58E+03	7.60E+03	1.19E+03	1.04E+03	1.57E+03	7.73E+02
$F 3$	标准差	4.56E+03	1.87E+03	6.28E+02	1.77E+03	1.27E+02	1.13E+02	3.32E+02	5.34E+01
$F 4$	平均值	1.74E+03	2.13E+03	1.69E+03	1.57E+03	1.37E+03	1.33E+03	1.35E+03	1.21E+03
$F 4$	标准差	1.04E+02	2.96E+01	2.74E+02	9.01E+01	6.65E+01	7.15E+01	6.93E+01	7.02E+01
$F 5$	平均值	6.71E+02	6.98E+02	6.68E+02	6.74E+02	6.57E+02	6.51E+02	6.59E+02	6.27E+02
$F 5$	标准差	5.52E+00	1.72E+00	1.56E+01	3.88E+00	2.14E+00	3.98E+00	2.42E+00	5.39E+00
$F 6$	平均值	4.70E+03	4.45E+03	2.87E+03	3.75E+03	3.37E+03	3.20E+03	3.36E+03	2.07E+03
$F 6$	标准差	6.64E+02	8.32E+01	2.63E+02	1.52E+02	2.36E+02	2.03E+02	2.73E+02	3.13E+02
$F 7$	平均值	2.06E+03	2.46E+03	1.91E+03	1.91E+03	1.65E+03	1.60E+03	1.69E+03	1.48E+03
$F 7$	标准差	1.48E+02	2.46E+01	2.71E+02	8.44E+01	6.50E+01	7.81E+01	4.83E+01	1.26E+02
$F 8$	平均值	6.96E+04	7.81E+04	6.98E+04	4.32E+04	2.58E+04	2.52E+04	2.46E+04	1.93E+04
$F 8$	标准差	2.95E+04	3.27E+03	1.29E+04	5.21E+03	1.38E+03	1.32E+03	2.17E+03	3.05E+03
$F 9$	平均值	2.17E+04	3.18E+04	2.78E+04	2.12E+04	1.79E+04	1.66E+04	1.65E+04	1.55E+04
$F 9$	标准差	1.33E+03	8.87E+02	5.83E+03	1.32E+03	4.00E+03	1.36E+03	1.38E+03	1.41E+03
$F 10$	平均值	1.35E+05	3.36E+05	1.80E+05	8.99E+04	1.41E+04	1.49E+04	1.13E+04	3.34E+03
$F 10$	标准差	5.67E+04	4.47E+04	3.60E+04	2.94E+04	3.72E+03	5.01E+03	4.96E+03	9.48E+02
$F 11$	平均值	7.22E+10	1.92E+11	4.34E+09	3.05E+10	1.12E+08	9.08E+07	1.79E+08	1.99E+07
$F 11$	标准差	2.27E+10	1.06E+10	1.61E+09	7.49E+09	5.50E+07	4.51E+07	7.34E+07	7.79E+06
$F 12$	平均值	6.08E+09	4.19E+10	1.77E+08	3.98E+09	8.04E+05	2.50E+04	3.01E+04	4.85E+03
$F 12$	标准差	2.04E+09	3.38E+09	1.76E+08	2.49E+09	2.44E+06	6.61E+03	1.36E+04	1.76E+03
$F 13$	平均值	1.90E+07	6.17E+07	1.21E+07	1.11E+07	5.95E+05	8.19E+05	5.73E+05	1.03E+06
$F 13$	标准差	2.32E+07	2.18E+07	7.93E+06	6.62E+06	3.09E+05	5.60E+05	2.80E+05	5.13E+05
$F 14$	平均值	3.15E+09	2.32E+10	5.61E+07	5.96E+08	1.30E+04	7.94E+03	8.14E+03	4.02E+03
$F 14$	标准差	1.35E+09	2.01E+09	7.85E+07	5.86E+08	5.61E+03	4.14E+03	2.90E+03	3.57E+03
$F 15$	平均值	9.52E+03	2.73E+04	8.43E+03	1.06E+04	7.25E+03	6.49E+03	7.65E+03	5.49E+03
$F 15$	标准差	1.01E+03	2.17E+03	1.25E+03	1.45E+03	5.04E+02	5.17E+02	8.78E+02	8.00E+02
$F 16$	平均值	1.94E+04	3.03E+06	8.29E+03	1.49E+04	5.97E+03	5.59E+03	5.91E+03	5.29E+03
$F 16$	标准差	1.40E+04	1.55E+06	9.41E+02	1.89E+04	6.60E+02	6.53E+02	5.69E+02	4.17E+02
$F 17$	平均值	4.63E+07	1.73E+08	1.94E+07	7.37E+06	1.26E+06	1.73E+06	9.56E+05	1.24E+06
$F 17$	标准差	3.39E+07	5.21E+07	1.96E+07	3.12E+06	5.34E+05	9.65E+05	5.02E+05	4.03E+05
$F 18$	平均值	2.76E+09	2.06E+10	4.22E+07	1.44E+09	1.00E+04	7.59E+03	1.24E+04	4.79E+03
$F 18$	标准差	2.11E+09	2.29E+09	2.94E+07	2.32E+09	5.86E+03	5.14E+03	9.50E+03	2.90E+03
$F 19$	平均值	6.25E+03	7.58E+03	6.68E+03	5.66E+03	5.52E+03	5.58E+03	5.48E+03	4.95E+03
$F 19$	标准差	8.55E+02	2.65E+02	8.20E+02	5.80E+02	4.79E+02	5.48E+02	4.53E+02	9.38E+02
$F 20$	平均值	3.80E+03	4.72E+03	3.98E+03	3.59E+03	3.49E+03	3.25E+03	3.49E+03	3.05E+03
$F 20$	标准差	1.29E+02	1.15E+02	1.45E+02	1.29E+02	1.88E+02	1.83E+02	1.79E+02	1.12E+02
$F 21$	平均值	2.34E+04	3.45E+04	2.71E+04	2.47E+04	2.49E+04	1.94E+04	2.16E+04	1.78E+04
$F 21$	标准差	2.22E+03	3.57E+02	4.47E+03	1.48E+03	3.98E+03	1.40E+03	3.08E+03	1.64E+03
$F 22$	平均值	4.91E+03	6.09E+03	4.66E+03	4.40E+03	4.16E+03	3.94E+03	4.38E+03	3.63E+03
$F 22$	标准差	1.25E+02	1.19E+02	2.47E+02	1.66E+02	1.53E+02	2.37E+02	2.23E+02	1.69E+02
$F 23$	平均值	6.51E+03	9.12E+03	6.17E+03	5.53E+03	5.07E+03	5.10E+03	5.17E+03	4.33E+03
$F 23$	标准差	3.84E+02	4.46E+02	3.52E+02	2.19E+02	5.43E+02	5.00E+02	4.25E+02	1.89E+02
$F 24$	平均值	1.85E+04	2.78E+04	6.97E+03	9.61E+03	3.98E+03	3.70E+03	4.36E+03	3.37E+03
$F 24$	标准差	5.14E+03	1.09E+03	4.41E+03	2.03E+03	2.41E+02	9.12E+01	3.92E+02	4.27E+01
$F 25$	平均值	3.60E+04	4.94E+04	2.63E+04	3.33E+04	2.69E+04	2.15E+04	2.77E+04	1.85E+04
$F 25$	标准差	3.97E+03	1.68E+03	2.76E+03	2.81E+03	2.35E+03	4.90E+03	4.41E+03	6.17E+03
$F 26$	平均值	5.61E+03	1.19E+04	4.39E+03	5.32E+03	4.12E+03	4.22E+03	4.28E+03	3.94E+03
$F 26$	标准差	3.71E+02	9.32E+02	5.29E+02	4.58E+02	3.67E+02	5.21E+02	2.57E+02	1.97E+02
$F 27$	平均值	2.70E+04	2.70E+04	1.68E+04	1.25E+04	3.95E+03	4.76E+03	4.53E+03	3.47E+03
$F 27$	标准差	2.04E+03	1.10E+03	6.42E+03	2.48E+03	1.16E+02	4.68E+02	4.92E+02	2.42E+01
$F 28$	平均值	2.28E+04	2.60E+05	1.11E+04	1.45E+04	8.34E+03	7.93E+03	8.97E+03	7.24E+03
$F 28$	标准差	2.23E+04	1.47E+05	1.96E+03	4.18E+03	7.60E+02	9.02E+02	9.51E+02	5.12E+02
$F 29$	平均值	6.57E+09	3.96E+10	1.38E+08	3.28E+09	8.47E+05	4.10E+05	2.38E+06	2.06E+04
$F 29$	标准差	2.82E+09	2.90E+09	1.05E+08	2.39E+09	6.67E+05	2.67E+05	1.61E+06	7.85E+03
Friedman	秩均值	6.74	7.88	5.53	5.60	3.29	2.55	3.26	1.14
Friedman	排名	7	8	5	6	4	2	3	1

表1

图2

表2

参数设置

参数	取值
路径节点个数 $n$	5
路径节点之间的插值点个数 $m$	1 000
路径节点和插值点的边界坐标	(200,100)~(1 200,1 100)
机器人最大宽度 $D$ /m	1
危险区圆环宽度 $S$ /m	1
安全因子 $a$	100
最大转角 $ϕ m a x$ /(°)	60
惩罚因子 $w$	10

表2

表3

图3

参考文献 34

1	Liu Jiahao, Gu Tao, Liao Zhixue. Operation Design of a Robot Logistics System Considering Demand Fluctuations in the Hotel Industry[J]. International Journal of Contemporary Hospitality Management, 2024, 36(1): 113-135.
2	Zhu Shengyan, Wang Huiwen, Zhang Xiaoqin, et al. A Decision Model on Human-robot Collaborative Routing for Automatic Logistics[J]. Advanced Engineering Informatics, 2022, 53: 101681.
3	Wang Yinchu, He Zhi, Cao Dandan, et al. Coverage Path Planning for Kiwifruit Picking Robots Based on Deep Reinforcement Learning[J]. Computers and Electronics in Agriculture, 2023, 205: 107593.
4	Choi Byunghun, Lee Wonsuk, Park Gyuhyun, et al. Development and Control of a Military Rescue Robot for Casualty Extraction Task[J]. Journal of Field Robotics, 2019, 36(4): 656-676.
5	Liu Lixing, Wang Xu, Yang Xin, et al. Path Planning Techniques for Mobile Robots: Review and Prospect[J]. Expert Systems with Applications, 2023, 227: 120254.
6	Sathyaraj B M, Jain L C, Finn A, et al. Multiple UAVs Path Planning Algorithms: A Comparative Study[J]. Fuzzy Optimization and Decision Making, 2008, 7(3): 257-267.
7	Bayili Serhat, Polat Faruk. Limited-damage A*: A Path Search Algorithm That Considers Damage as a Feasibility Criterion[J]. Knowledge-Based Systems, 2011, 24(4): 501-512.
8	Zhang Xiangyin, Duan Haibin, Luo Qinan. Levenberg-marquardt Based Artificial Physics Method for Mobile Robot Oscillation Alleviation[J]. Science China. Physics, Mechanics & Astronomy, 2014, 57(9): 1771-1777.
9	Mohd Nadhir Ab Wahab, Nefti-Meziani S, Atyabi A. A Comparative Review on Mobile Robot Path Planning: Classical or Meta-heuristic Methods?[J]. Annual Reviews in Control, 2020, 50: 233-252.
10	Atyabi Adham, Phon-Amnuaisuk Somnuk, Chin Kuan Ho. Navigating a Robotic Swarm in an Uncharted 2D Landscape[J]. Applied Soft Computing, 2010, 10(1): 149-169.
11	Sathiya V, Chinnadurai M, Ramabalan S. Mobile Robot Path Planning Using Fuzzy Enhanced Improved Multi-objective Particle Swarm Optimization (FIMOPSO)[J]. Expert Systems with Applications, 2022, 198: 116875.
12	Agarwal Divya, Bharti Pushpendra S. Implementing Modified Swarm Intelligence Algorithm Based on Slime Moulds for Path Planning and Obstacle Avoidance Problem in Mobile Robots[J]. Applied Soft Computing, 2021, 107: 107372.
13	Cui Yibing, Hu Wei, Rahmani Ahmed. A Reinforcement Learning Based Artificial Bee Colony Algorithm with Application in Robot Path Planning[J]. Expert Systems with Applications, 2022, 203: 117389.
14	Rahmaniar Wahyu, Amalia Eka Rakhmania. Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms[J]. Journal of Robotics and Control, 2022, 3(1): 1-7.
15	Yu Zhiheng, Yuan Jian, Li Yongsheng, et al. A Path Planning Algorithm for Mobile Robot Based on Water Flow Potential Field Method and Beetle Antennae Search Algorithm[J]. Computers and Electrical Engineering, 2023, 109, Part B: 108730.
16	Abdollahzadeh Benyamin, Farhad Soleimanian Gharehchopogh, Mirjalili Seyedali. Artificial Gorilla Troops Optimizer: A New Nature-inspired Metaheuristic Algorithm for Global Optimization Problems[J]. International Journal of Intelligent Systems, 2021, 36(10): 5887-5958.
17	Alsolai Hadeel, Alzahrani Jaber S, Maray Mohammed, et al. Enhanced Artificial Gorilla Troops Optimizer Based Clustering Protocol for UAV-assisted Intelligent Vehicular Network[J]. Drones, 2022, 6(11): 358.
18	El-Dabah Mahmoud A, Hassan Mohamed H, Kamel Salah, et al. Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer[J]. IEEE Access, 2022, 10: 82560-82579.
19	Rawa Muhyaddin, Abusorrah Abdullah, Al-Turki Yusuf, et al. Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer[J]. Mathematics, 2022, 10(7): 1057.
20	Mostafa Reham R, Gaheen Marwa A, Mohamed Abd ElAziz, et al. An Improved Gorilla Troops Optimizer for Global Optimization Problems and Feature Selection[J]. Knowledge-Based Systems, 2023, 269: 110462.
21	杨模, 刘紫燕, 梁静, 等. 基于Pareto支配的改进人工大猩猩部队多目标优化[J]. 传感技术学报, 2023, 36(4): 590-601.
	Yang Mo, Liu Ziyan, Liang Jing, et al. Multi-objective Optimization of Improved Artificial Gorilla Troops Based on Pareto Domination[J]. Chinese Journal of Sensors and Actuators, 2023, 36(4): 590-601.
22	刘景森, 吉宏远, 李煜. 基于改进蝙蝠算法和三次样条插值的机器人路径规划[J]. 自动化学报, 2021, 47(7): 1710-1719.
	Liu Jingsen, Ji Hongyuan, Li Yu. Robot Path Planning Based on Improved Bat Algorithm and Cubic Spline Interpolation[J]. Acta Automatica Sinica, 2021, 47(7): 1710-1719.
23	Sun Yongjun, Yang Tong, Liu Zujun. A Whale Optimization Algorithm Based on Quadratic Interpolation for High-dimensional Global Optimization Problems[J]. Applied Soft Computing, 2019, 85: 105744.
24	Guo Wenyan, Wang Yuan, Dai Fang, et al. Improved Sine Cosine Algorithm Combined with Optimal Neighborhood and Quadratic Interpolation Strategy[J]. Engineering Applications of Artificial Intelligence, 2020, 94: 103779.
25	Yang Xiao, Wang Rui, Zhao Dong, et al. An Adaptive Quadratic Interpolation and Rounding Mechanism Sine Cosine Algorithm with Application to Constrained Engineering Optimization Problems[J]. Expert Systems with Applications, 2023, 213, Part B: 119041.
26	Forrest Stephanie. Genetic Algorithms[J]. ACM Computing Surveys, 1996, 28(1): 77-80.
27	Wang Wentao, Ye Chen, Tian Jun. SGGTSO: A Spherical Vector-based Optimization Algorithm for 3D UAV Path Planning[J]. Drones, 2023, 7(7): 452.
28	Awad N H, Ali M Z, Liang J J, et al. Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-parameter Numerical Optimization[M]//Technical Report. Nanyang Technological University Singapore, 2016: 1-34.
29	Shaheen A M, Ginidi A R, El-Sehiemy R A, et al. Optimal Parameters Extraction of Photovoltaic Triple Diode Model Using an Enhanced Artificial Gorilla Troops Optimizer[J]. Energy, 2023, 283: 129034.
30	Hassan M H, Kamel S, Mohamed A W. Enhanced Gorilla Troops Optimizer Powered by Marine Predator Algorithm: Global Optimization and Engineering Design[J]. Scientific Reports, 2024, 14(1): 7650.
31	Bao G Q, Mao K F. Particle Swarm Optimization Algorithm with Asymmetric Time Varying Acceleration Coefficients[C]//2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). Piscataway: IEEE, 2009: 2134-2139.
32	Zhong Changting, Li Gang, Meng Zeng. Beluga Whale Optimization: A Novel Nature-inspired Metaheuristic Algorithm[J]. Knowledge-Based Systems, 2022, 251: 109215.
33	Xue Jiankai, Shen Bo. Dung Beetle Optimizer: A New Meta-heuristic Algorithm for Global Optimization[J]. The Journal of Supercomputing, 2023, 79(7): 7305-7336.
34	Seyyedabbasi Amir, Kiani Farzad. Sand Cat Swarm Optimization: A Nature-inspired Algorithm to Solve Global Optimization Problems[J]. Engineering with Computers, 2023, 39(4): 2627-2651.

场景	性能指标	MPSO	BWO	DBO	SCSO	GTO	EGTO	EAGT	QGGTO
I	平均值	1 727.94	1 663.24	1 801.72	1 589.38	1 439.17	1 729.04	1 429.75	1 428.83
I	标准差	126.32	98.16	180.25	70.32	38.54	37.18	26.32	23.37
II	平均值	1 518.33	1 878.73	1 618.78	1 808.25	1 500.73	1 760.97	1 592.47	1 475.17
II	标准差	42.54	215.75	91.09	191.98	76.77	220.73	115.76	21.89
III	平均值	1 810.20	2 675.82	1 739.81	2 009.58	1 673.67	2 015.01	1 730.05	1 658.61
III	标准差	138.21	382.26	127.85	121.24	159.24	190.27	118.43	147.69
IV	平均值	1 711.53	2 575.28	1 701.21	1 838.54	1 636.47	2 109.99	1 695.24	1 613.04
IV	标准差	142.47	302.02	99.02	145.48	170.85	314.22	141.91	186.90
Friedman	秩均值	4.75	7.75	5.25	5.75	2.25	6.75	3.00	1.00
Friedman	排名	4	8	5	6	2	7	3	1

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[4]	邓向阳, 张立民, 方伟, 汤淼. 基于双向汇聚引导蚁群算法的机器人路径规划[J]. 系统仿真学报, 2022, 34(5): 1101-1108.
[5]	乔乔, 王艳, 纪志成. 基于冲突搜索算法的多机器人路径规划[J]. 系统仿真学报, 2022, 34(12): 2659-2669.