Multi-Vehicle Close Enough Orienteering Problem with Bézier Curves for Multi-Rotor Aerial Vehicles

This paper introduces the Close Enough Orienteering Problem (CEOP) for planning missions with multi-rotor aerial vehicles considering their maximal velocity and acceleration limits. The addressed problem stands to select the most rewarding target locations and sequence to visit them in the given limited travel budget. The reward is collected within a non-zero range from a particular target location that allows saving the travel cost, and thus collect more rewards. Hence, we are searching for the fastest trajectories to collect the most valuable rewards such that the motion constraints are not violated, and the travel budget is satisfied. We leverage on existing trajectory parametrization based on Bézier curves recently deployed in surveillance planning using unsupervised learning, and we propose to employ the learning in a solution of the introduced multi-vehicle CEOP. Feasibility of the proposed approach is supported by empirical evaluation and experimental deployment using multi-rotor vehicles.