[1]刘中常,李国良.基于群体智能优化算法的移动机器人分组聚集方法[J].郑州大学学报(工学版),2025,46(02):35-42.[doi:10.13705/j.issn.1671-6833.2025.02.021]
　LIU Zhongchang,LI Guoliang.Group Aggregation Method of Mobile Robots Based on Swarm Intelligence Optimization Algorithm[J].Journal of Zhengzhou University (Engineering Science),2025,46(02):35-42.[doi:10.13705/j.issn.1671-6833.2025.02.021]
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基于群体智能优化算法的移动机器人分组聚集方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年02期
页码:
35-42
栏目:
出版日期:
2025-03-10

文章信息/Info

Title:
Group Aggregation Method of Mobile Robots Based on Swarm Intelligence Optimization Algorithm
文章编号:
1671-6833(2025)02-0035-08
作者:
刘中常;  李国良
大连海事大学 船舶电气工程学院,辽宁 大连 116026
Author(s):
LIU Zhongchang;  LI Guoliang
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
关键词:
移动机器人集群;  导航方法;  分组聚集;  灰狼优化算法;  有限感知
Keywords:
mobile robot swarm;  navigation method;  group aggregation;  GWO algorithm;  limited sensing range
分类号:
TP242TP273
DOI:
10.13705/j.issn.1671-6833.2025.02.021
文献标志码:
A
摘要:
为了应对多个混合移动机器人集群在进行分组聚集时速度慢、成功率低等问题,设计了一种基于群体智能优化算法的分散式的分组聚集导航方法,使得每个机器人只利用有限感知范围内其他机器人的信息,结合灰狼优化(GWO)算法的原理来计算其与同组其他机器人进行聚集的首选导航速度。在确定每个机器人可利用的局部信息时,根据各组机器人的信息共享意愿,分为不同组之间相互合作和相互独立两种情况分别进行设计,从而实现了对原始的集中式GWO算法的分散化处理与应用。进一步对原始GWO算法进行改进,通过采用非线性收敛因子来提高机器人的前期探索能力,从而增大与同组成员的相遇概率,最终提高分组聚集的成功率。为了避免移动过程中机器人之间发生相互碰撞,利用分散式的最优互惠避碰(ORCA)算法对每个机器人的首选导航速度进行修正。仿真结果显示了所设计的不同分组聚集导航算法的有效性,并且相较于现有的基于粒子群优化(PSO)算法的分组聚集方法,所设计的各算法均具有更高的成功率、更快的收敛速度及更强的稳定性。
Abstract:
To cope with the issues of low convergence speed and low success rate in the realization of segregating multiple coupled subgroups, a swarm intelligence optimization inspired method was proposed to provide a decentralized navigation method for the robots. The designed navigation algorithm enabled each robot to utilize information of the other robots within a limited sensing range, and calculated the preferred navigation speed for its aggregation with other robots of the same subgroup by following principles of the grey wolf optimization (GWO) algorithm. Based on the inclination of information sharing among different subgroups, the applicable information for each robot was determined separately for the cases of being cooperative or being independent among different subgroups, so that the original centralized GWO algorithm could be applied in a decentralized manner. Furthermore, the original GWO algorithm was improved by using a nonlinearly convergence factor which could improved the exploration ability of the robots. In this way, the chance of encountering with group members for each robot was increased, and eventually improved the success rate of group aggregation. In order to avoid collisions between robots during the moving process, in this study the decentralized optimal reciprocal collision avoidance (ORCA) algorithm was used to adjust the preferred navigation speed of each robot. Simulation results demonstrated the effectiveness of the designed group aggregation navigation algorithm, showing higher success rates, faster convergence speed, and greater stability compared to an existing particle swarm optimization (PSO) algorithm-based method.

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更新日期/Last Update: 2025-03-13