Maximizing mutual information for multipass target search in changing environments
This paper presents a receding-horizon cooperative search algorithm that jointly optimizes routes and sensor orientations for a team of autonomous agents searching for a mobile target in a closed and bounded region. By sampling this region at locations with high target probability at each time step, we reduce the continuous search problem to a sequence of optimizations on a finite, dynamically updated graph whose vertices represent waypoints for the searchers and whose edges indicate potential connections between the waypoints. Paths are computed on this graph using a receding horizon approach, in which the horizon is a fixed number of graph vertices. To facilitate a fair comparison between paths of varying length on non-uniform graphs, the optimization criterion measures the probability of finding the target per unit travel time. Using this algorithm, we show that the team discovers the target in finite time with probability one. Simulations verify that this algorithm makes effective use of agents and outperforms previously proposed search algorithms. We have successfully hardware tested this algorithm in two small UAVs with gimbaled video cameras.