针对路径干扰的多机器人分层协作k鲁棒路径规划

doi:10.16182/j.issn1004731x.joss.24-0229

摘要/Abstract

摘要：

为在干扰环境下规划多机器人的无冲突路径，基于多机器人k鲁棒路径规划，设计了多机器人分层协作k鲁棒路径规划框架。在优先级优化层，针对求解顺序引发的起步困境问题，根据封闭因子确定多机器人路径求解顺序；在多机鲁棒协调层，以提高求解效率为目标，引入安全区间作为k鲁棒和无碰撞设计的基础，给出k鲁棒意义下的多机器人无碰撞路径约束；在核心求解层，采用动态的阻力因子，在规划时引导高优先级机器人规避低优先级机器人的起步区域。实验结果表明：所提算法能够以较小的路径损耗有效降低k鲁棒起步困境的影响，k鲁棒路径求解成功率较现有先进算法平均可提高39%以上。

关键词: 多机器人系统, 路径规划, 鲁棒规划, 延时, 干扰

Abstract:

To plan collision-free paths for multiple robots in interference environments, based on the multi-robotk-robust path planning, this paper designed a multi-robot hierarchical collaborative k-robust path planning framework. In the priority optimization layer, in response to the starting predicament caused by the solution sequence, the multi-robot path solving sequence was determined based on the closure factor. In the multi-robot robust coordination layer, with the goal of improving solution efficiency, a safety interval was introduced as the basis for the design of k-robustness and collision-free avoidance. A collision-free path constraint for multiple robots in the sense of k-robustness was given. In the core solution layer, dynamic resistance factors were used to guide high priority robots to avoid the starting area of low priority robots during planning. The test results show that the proposed algorithm can effectively reduce the impact ofk-robust starting predicament with small path loss, and the success rate ofk-robust path solutions can be improved by more than 39% on average compared to existing advanced algorithms.

Key words: multi-robot system, path planning, robust planning, delay, interference

中图分类号:

TP242

张凯翔,毛剑琳,王妮娅等 . 针对路径干扰的多机器人分层协作k鲁棒路径规划[J]. 系统仿真学报, 2025, 37(8): 2074-2088.

Zhang Kaixiang,Mao Jianlin,Wang Niya,et al . Multi-robot Hierarchical Collaborative k-robust Path Planning for Path Interference[J]. Journal of System Simulation, 2025, 37(8): 2074-2088.

图/表 16

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

表2

k=5时IkR-SIPP算法求解路径方案

机器人	路径	具体方案
a₁	$π 1$	{(1,2), (2,2), (2,2), (2,2), (2,2), (2,2), (2,2), (2,2), (2,2), (2,2), (3,2), (4,2), (5,2), (6,2), (6,3), (6,4), (6,5), (6,6)}
a₂	$π 2$	{(8,3), (8,4), (8,5), (7,5)}
a₃	$π 3$	{(6,4), (7,4), (7,4), (7,4), (7,4), (7,4), (7,4), (8,4), (8,5), (8,6)}
a₄	$π 4$	{(6,1), (5,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,1), (4,2), (3,2), (2,2), (1,2), (1,1)}
a₅	$π 5$	{(6,3), (6,2), (5,2), (4,2), (3,2), (3,3), (3,4), (4,4), (4,5)}
a₆	$π 6$	{(2,5), (2,6), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,7), (2,6), (2,5), (2,4), (3,4), (3,3), (3,2), (4,2), (5,2), (5,1)}
a₇	$π 7$	{(1,4), (2,4), (2,4), (2,4), (2,4), (2,4), (2,5), (2,6), (3,6), (3,7)}
a₈	$π 8$	{(2,3), (2,3), (2,3), (2,3), (2,3), (2,3), (2,3), (2,3), (2,3), (2,3), (2,3), (2,4), (2,5), (2,6), (1,6)}

表2

图10

图11

图12

图13

图14

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k值	求解时间/ms					SoC/步
k值	kR-EPEA^*	kR-CBS	IkR-CBS	kR-SIPP	IkR-SIPP	kR-EPEA^*	kR-CBS	IkR-CBS	kR-SIPP	IkR-SIPP
0	32.53	35.25	32.51	1.92	1.96	49	49	49	52	51
1	50 273.51	49 744.77	864.16	2.02	2.03	55	55	55	60	60
2	NA	NA	59 105.80	2.09	2.28	NA	NA	63	74	78
3	NA	NA	NA	2.40	2.46	NA	NA	NA	103	88
4	NA	NA	NA	NA	2.34	NA	NA	NA	NA	98
5	NA	NA	NA	NA	2.62	NA	NA	NA	NA	108