Group Aggregation Method of Mobile Robots Based on Swarm Intelligence Optimization Algorithm

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Group Aggregation Method of Mobile Robots Based on Swarm Intelligence Optimization Algorithm

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 02 Page number: 35-42 Column: Public date: 2025-03-10

Title:: Group Aggregation Method of Mobile Robots Based on Swarm Intelligence Optimization Algorithm

Author(s):: LIU Zhongchang; LI Guoliang; College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China

Keywords:: mobile robot swarm; navigation method; group aggregation; GWO algorithm; limited sensing range

CLC:: TP242TP273

DOI:: 10.13705/j.issn.1671-6833.2025.02.021

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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Last Update: 2025-03-13