首页 /研究 /Hybrid Formation Control for Multi-Robot Hunters Based on Multi-Agent Deep Deterministic Policy Gradient
SWARM

Hybrid Formation Control for Multi-Robot Hunters Based on Multi-Agent Deep Deterministic Policy Gradient

Oussama Hamed, Mohamed Hamlich

发表年份
2021
引用次数
8
访问权限
开放获取

摘要

The cooperation between mobile robots is one of the most important topics of interest to researchers, especially in the many areas in which it can be applied. Hunting a moving target with random behavior is an application that requires robust cooperation between several robots in the multi-robot system. This paper proposed a hybrid formation control for hunting a dynamic target which is based on wolves’ hunting behavior in order to search and capture the prey quickly and avoid its escape and Multi Agent Deep Deterministic Policy Gradient (MADDPG) to plan an optimal accessible path to the desired position. The validity and the effectiveness of the proposed formation control are demonstrated with simulation results.

关键词

Computer scienceRobotPosition (finance)Control (management)Mobile robotPlan (archaeology)Path (computing)Motion planningArtificial intelligenceGeography

相关论文

查看 SWARM 分类全部论文