Heuristic-Based Multiple Mobile Depots Route Planning for Recharging Persistent Surveillance Robots

Abstract

Persistent surveillance of a target space using multiple unmanned aerial vehicles (UAVs) has multiple applications such as geographical surveys, air quality monitoring, and security monitoring. The limited onboard battery capacity challenges the continuous operation in these applications of persistent robots. We consider the problem for recharging persistent robots using mobile depots. The mobile depots collectively compute a set of tours to recharge all persistent robots with the minimum total cost. Compared to other works, the persistent UAVs are not required to detour to a static depot for energy replenishment such as recharging or battery swapping. We formulate this problem as a Generalized Multiple Depots Travelling Salesman Problem (GMDTSP) on a complete graph. A heuristic-based algorithm Multiple Depots Random Select (RSMD) is proposed to solve the recharging problem efficiently. The RSMD has proved to have an analytical constant upper bound in the worst-case scenario. We also propose a post-processing heuristic (RSMD-IM) to improve the solution quality further. We demonstrate the efficiency and effectiveness of our algorithm via benchmark on multiple instances from TSPLIB and GTSPLIB. The simulation results show that RSMD and RSMD-IM will perform significantly faster than the state of the art heuristic solver LKH with a loss of about 10% of solution quality.