A Cooperative Aerial Inspection System with Continuable Charging Strategy Compared to a single flying robot, the swarm can significantly enhance robustness and capability in realworld missions. However, the overall flight duration of such system is still heavily constrained by the energy system, typically composed of lithium batteries. To cope with this issue, this paper develops an autonomous mobile charging station for the aerial swarm system to improve the endurance so that the system satisfies the need for the longterm mission such as surveillance and monitoring. The mobile charging station has the ability of environment perception and obstacle avoidance. On this basis, a charging module with multiple contact points is developed, and a motion tracking algorithm is proposed accordingly to support precise landing on the charging module. To further improve the flexibility in ongoing realworld missions, a charging schedule and an area coverage policy are also proposed. A prototype system is built to verify the effectiveness of this system and experiments demonstrate the system could cover an area of interest completely and efficiently.

Heuristicbased Multiple Mobile Depots Route Planning for Recharging Persistent Surveillance Robots 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 heuristicbased 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 worstcase scenario. We also propose a postprocessing heuristic (RSMDIM) 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 RSMDIM will perform significantly faster than the state of the art heuristic solver LKH with a loss of about 10% of solution quality.