Cooperative Sensor Scheduling for Long Term Planning

We present a sensor scheduling algorithm to plan the motion of multiple autonomous platforms for cooperative tracking of targets within a region that contains obstacles and occlusions. The platforms have kinematic constraints and their sensors have restricted field of view and range. The proposed algorithm is a variant of the Rapidly exploring Random Tree star algorithm (RRT*) that has been adapted to the problem of determining paths for multiple independent kinematically constrained platforms to optimise their tracking performance. To guide the scheduling algorithm, we define a tracking cost based on the Posterior Cramér Rao Bound (PCRB) derived from the predicted positions of the platforms and targets. Through simulations of generated paths, we show that the algorithm generates rational plans for tracking targets and that the tracking cost accurately predicts the realised performance of the platforms.