Real-time Adaptive Planning for Intelligent Distributed TSP-inspired Drone Swarming (RAPID-TSP)

This paper presents RAPID-TSP, a distributed coordination algorithm for multi-drone scanning missions in dynamic environments. It enables drones to balance area coverage efficiency with network connectivity by leveraging a tunable parameter λ. This parameter allows each drone to adaptively choose its next scanning target based on proximity and the positions of neighboring drones, while aiming to maintain connectivity with a base station for mission updates. RAPID-TSP incorporates conflict resolution and supports two swarming strategies to minimize redundant scanning and ensure efficient collaboration. A realistic scenario involving the scanning of container stacks in a logistics hub serves as evaluation scenario. Extensive simulations show how RAPID-TSP performs robustly under varying conditions and can reduce mission completion time by up to 40% compared to traditional mTSP coordination methods. A key design insight is that mission time is significantly reduced by adding more drones only when communication range is sufficient; otherwise, sparse connectivity limits their effectiveness.