Hybrid Adaptive Greedy Algorithm Addressing the Multi-Robot Path Planning Problem

Abstract

In the past few years, path planning and scheduling became a high-impact research topic due to their real-world applications such as transportation, manufacturing and robotics. This paper focuses on the Multi-robot Path Planning (MPP) problem, which consists of planning the route for a set of robots in a given static environment. The main goal is to navigate the robots from a starting point to a destination point without colliding with other robots or static obstacles. We propose a hybrid method -- H* -- that combines adaptive route planning based on A* and local search algorithm to optimize routes in the context of the MPP problem. The A* algorithm finds the optimal solution for the route search problem and a heuristic approach is applied to scale up to the multi-agent scenario.The overall length of determined paths and the number of robot collisions is minimized during the evaluations specific small-scale environments.Computational experiments are conducted for multi-robot scenarios and the performance of H* is compared to several path-searching algorithms including A* variations extended for the multi-agent scenario and coevolutionary algorithms.Experimental results demonstrate that H* outperforms the A* based heuristic approaches in terms of path length. H* shows similar performance as the coevolutionary method and performs better on smaller-scale maps.