Motion Planning and Task Allocation for a Jumping Rover Team

Abstract

This paper presents a cooperative robotic team composed of unmanned ground vehicles (UGVs) with hybrid operational modes to tackle the multiple traveling salesman problem (mTSP) with obstacles. The hybrid operational modes allow every UGV in the team to not only travel on a ground surface but also jump over obstacles. We name these UGVs jumping rovers. The jumping capability provides a flexible form of locomotion by leaping and landing on top of obstacles instead of navigating around obstacles. To solve the mTSP, an optimal path between any two objective points in an mTSP is determined by the optimized rapidly-exploring random tree method, named RRT*, and is further improved through a refined RRT* algorithm to find a smoother path between targets. We then formulate the mTSP as a mixed-integer linear programming (MILP) problem to search for the most cost-effective combination of paths for multiple UGVs. The effectiveness of the hybrid operational modes and optimized motion with assigned tasks is verified in an indoor, physical experimental environment using the customized jumping rovers.