Improved artificial fish swarm based optimize rapidly-exploring random trees multi-robot exploration algorithm

Abstract

To solve the problems of high storage resource consumption and low efficiency of the RRT exploration algorithm in the late stage of exploration, this paper proposes an Improved Artificial Fish Swarm based Optimize Rapidly-exploring Random Trees multi-robot Exploration Algorithm. Firstly, the efficiency of a single robot’s exploration of nearby unknown regions is improved by dynamically adjusting the step size of the RRT tree.Secondly, the improved artificial fish swarm algorithm is used to delete the redundant nodes in the RRT tree and optimize the node state in the RRT tree, which reduces the occupation of memory resources and improves the exploration efficiency of the RRT tree in the narrow environment.Results from comparative experiments in simulation environments with different degrees of openness show that the optimized exploration algorithm can save significant storage resources and show better exploration performance in narrow environments compared to the original RRT exploration algorithm.