Dynamic Multi-UAV Path Planning for Multi-Target Search and Connectivity

In this work, we propose and analyze multi-drone path planners for multi-target search and connectivity. The goal of the unmanned aerial vehicle (UAV) mission is to search an unknown area to detect, connect and monitor multiple randomly distributed targets to the ground control station (GCS) while maintaining the connectivity of the UAVs to GCS. To this end, we propose to use two types of UAVs: search and relay. The search drones scan the area via onboard sensors, whereas relay UAVs provide connectivity. We propose three different responses to target detection with increasing adaptability: (i) follow pre-planned paths and inform GCS when possible, (ii) follow pre-planned paths and inject new UAVs to monitor the detected targets, (iii) assign a search UAV to monitor target, and re-plan remaining UAV paths. Furthermore, we implement multi-objective optimization-based planners for single-type UAVs, where the paths are optimized in terms of total coverage time and percentage connectivity. Our results show that less adaptive schemes detect all targets faster; however, the connectivity of the UAVs and the targets to GCS is significantly better for the adaptive schemes. The joint-optimization methods, on the other hand, in their basic form, trade-off detection time to improve connectivity. When they are used in conjunction with relays, the connectivity performance is improved; however, the overall mission times may be up to 40% higher than our proposed methods, depending on the number of UAVs and targets.