Informative Trajectory Planning in Spatiotemporal Fields using Kinodynamic RRT*

This paper proposes a sampling based kinodynamic planning technique for planning informative trajectories for a sensing robot in a spatiotemporal environmental field. The robot uses a Kalman-Bucy filter to estimate the spatiotemporal field. The proposed algorithm finds a trajectory for the robot that minimizes a metric on the error covariance matrix of the field estimate. Since the error covariance matrix evolves according to the nonlinear Riccati differential equation, this requires planning a trajectory under a difficult differential constraint. The paper adapts kinodynamic RRT* to find the optimal robot trajectory by treating the Riccati differential equation as a kinodynamic constraint. This algorithm is proved to maintain the asymptotic optimality guarantee of the original kinodynamic RRT*. Numerical simulations and experimental trials with an m3pi robot demonstrate the effectiveness of the algorithm.