Path Planning based on Probabilistic Roadmap for Initial Deployment of Marsupial Robot Team

Abstract

In this paper, we present a new path planning approach for the initial deployment of a heterogeneous marsupial robot team consisting of a large-scale carrier and multiple rovers, and for efficient operation of a multi-robot system. In this robot team, the roles are explicitly divided. The carrier oversees the transportation, deployment, and retrieval of the rovers, whereas the rovers are responsible for performing tasks such as reconnaissance and search and rescue. We aim to use this robot team to minimize the maximum amount of time that each rover requires to reach a given task location. However, only a few studies have addressed pathplanning for robot teams. To generate an efficient path, the mobility and energy limitations of the robots and the obstacles in the environment must be considered. Because considering all possible paths on a graph is equivalent to solving a combinatorial optimization problem, we suggest a computationally efficient heuristic algorithm, which combines a greedy clustering method with a probabilistic roadmap. The proposed method is verified through simulations.