Exploration-Based Search for an Unknown Number of Targets using a UAV

We consider a scenario in which a UAV must locate an unknown number of targets at unknown locations in a 2D environment. A random finite set formulation with a particle filter is used to estimate the target locations from noisy measurements that may miss targets. A novel planning algorithm selects a next UAV state that maximizes an objective function consisting of two components: target refinement and an exploration. Found targets are saved and then disregarded from measurements to focus on refining poorly seen targets. The desired next state is used as a reference point for a nonlinear tracking controller for the robot. Simulation results show that the method works better than lawnmower and mutual-information baselines.