Distributed Cooperative Search using Information-Theoretic Costs for Particle Filters, with Quadrotor Applications ∗

Search and rescue missions can be efficiently and automatically performed by small, highly maneuverable unmanned aerial vehicle (UAV) teams. The search problem is complicated by a lack of prior information, nonlinear mapping between sensor observations and the physical world, and potentially non-Gaussian sensor noise models. To address these problems, a distributed control algorithm is proposed, using information theoretic methods with particle filters, to compute optimal control inputs for a multi-vehicle, coordinated localization of a stationary target. This technique exploits the structure of the probability distributions of the target state and of the sensor measurements to compute the control inputs that maneuver the UAVs to make observations that minimize the expected future uncertainty of the target state. Because the method directly uses the particle filter state and an accurate sensor noise model to compute the mutual information, it is no longer necessary to discard information by using linear and Gaussian approximations. To ensure safety of the vehicles, the algorithm incorporates collision avoidance and control authority constraints. The resulting information theoretic cost calculation is coupled amongst the vehicles and becomes prohibitive as the size of the UAV team becomes large. Therefore, single vehicle and pairwise approximations to the cost function are used that greatly reduce the computational burden and allow for development of a distributed algorithm for real-time optimization of vehicle trajectories. Simulation results are shown for a bearings-only sensor model with multiple vehicles. Initial flight tests of the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) show the feasibility of implementation of this algorithm on the quadrotor testbed and in real world situations.