Cooperative Dual-Task Path Planning for Persistent Surveillance and Emergency Handling by Multiple Unmanned Ground Vehicles

Road network persistent surveillance requires a swarm of unmanned ground vehicles (UGVs) to repeatedly surveil a sequence of places called viewpoints on a road network and detect randomly occurring events and problems at viewpoints. In most existing work, UGVs are responsible for detection but not handling emergencies like extinguishing fires, capturing intruders, or managing pollution. Hence, existing methods fail to perform both persistent surveillance and emergency handling. This work copes with both and proposes a cooperative dual-task path planning method for multiple UGVs. UGVs first treat all the viewpoints as candidates and compute their rewards to select their targets. Emergencies have a higher reward, allowing UGVs to solve dual tasks and prioritize emergencies. A heuristic protocol is then employed to choose appropriate UGVs for emergencies. Local path planning is finally considered when UGVs plan paths to targets to avoid deadlock and backtracking. Thus, a necessary and appropriate subswarm of UGVs with the nearest distance to emergencies are selected to handle them, while others continue surveilling viewpoints for potential emergencies, avoiding any long delayed handling of emergencies. The performance of the proposed method is evaluated in three road networks. The simulation and analysis results demonstrate its superiority over the state-of-the-art surveillance methods.