Remote Sensing with UAV and Mobile Recharging Vehicle Rendezvous
Michael H. Ostertag, Jason Ma, and Tajana Rosing
ABSTRACT
Small unmanned aerial vehicles (UAVs) equipped with sensors offer an effective way to perform high-resolution environmental monitoring in remote areas but suffer from limited battery life. In order to perform large-scale remote sensing, a UAV must cover the area using multiple discharge cycles. A practical and efficient method to achieve full coverage is for the sensing UAV to rendezvous with a mobile recharge vehicle (MRV) for a battery exchange, which is an NP-hard problem. Existing works tackle this problem using slow genetic algorithms or greedy heuristics.We propose an alternative approach: a two-stage algorithm that iterates between dividing a region into independent subregions aligned to MRV travel and a new diffusion heuristic that performs a local exchange of points of interest between neighboring subregions. The algorithm outperforms existing stateof- the-art planners for remote sensing applications, creating more fuel efficient paths that better align with MRV travel.
