Paper accepted in Transactions on Vehicular Technology

Our paper entitled “Dynamic Multi-UAV Path Planning for Multi-Target Search and Connectivity” is accepted for publication in IEEE TVT.

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.

Paper accepted in Ad Hoc Networks Journal

Our paper entitled “Multi-objective path planning for multi-UAV connectivity and area coverage” is accepted for publication in Ad Hoc Networks journal.

In this paper, we propose a multi-drone path planner that jointly optimizes area coverage time and connectivity among the drones. We propose a novel connectivity metric that includes not only percentage connectivity of the drones to GCS, but also the maximum duration of consecutive time that the drones are disconnected from the GCS. To solve this optimization formulation, we propose a multi-objective evolutionary algorithm with novel operations. We use our solver to test single, two and many objective path planning problems and compare our Pareto-optimal solutions to benchmark weighted-sum based solutions.