Moving Target Estimation and Active Tracking in Multi-Robot Systems

摘要

In this paper, we propose a comprehensive solution for 3-D active target tracking with multiple robots in a fully distributed setting. Here multiple robots cooperatively estimate their own states and the target's state and actively plan their motions to achieve better estimation of the target. For cooperative localization and target state estimation, each robot maintains a state vector consisting of its own state, the target's state, and its own cloned history states. The challenge of localizing moving robots in 3-D is addressed by using multi-robot cooperative visual-inertial odometry algorithm, which improves the estimation accuracy by using environmental common feature measurements. Each robot's target measurement (if available) and its neighbors' target estimators are then exploited for estimation updates. To preserve and update the correlations between the target and robot states while limiting the influence of bad target estimates on localization accuracy, the Schmidt-Kalman Filter framework is adopted. For motion planning, a gradient-based approach that uses differentiable field-of-view and potential functions is employed to achieve efficient and accurate active target tracking while avoiding collisions and maintaining communication connectivity. Numerous simulations show that our proposed algorithm provides an accurate and efficient solution for cooperative localization and active target tracking.