Information driven path planning and control for collaborative aerial robotic sensors using artificial potential functions

Abstract

A path planning and control method based on adaptive potential functions is presented for a group of unmanned aerial vehicles (UAVs) equipped with onboard sensors, and deployed to search and classify multiple targets. The proposed method plans the motion of the UAVs to support a primary sensing objective that, in this case, is to maximize the classification performance of the sensor measurements gathered by the UAVs over time. An adaptive potential function approach originally developed for ground robots is modified and employed as a guidance law for a class of rotary-wing UAVs that must also avoid obstacles located in a three-dimensional workspace. The simulation results show that, by this approach, a single UAV is capable of visiting targets that offer the best tradeoff between distance and measurement information value. Furthermore, simulations involving multiple UAVs deployed to classify the same set of targets show that, by this approach, there emerge a cooperative behavior by which the UAVs can react, as a group, to the targets' classification uncertainties.