Minimum-Time Trajectory Planning Under Intermittent Measurements

This letter focuses on finding robust paths for a robotic system by taking into account the state uncertainty and the probability of collision. We are interested in dealing with intermittent exteroceptive measurements (e.g., collected from vision). We assume that these cues provide reliable measurements that will update a state estimation algorithm wherever they are available. The planner has to manage two tasks: reaching the goal in a minimum time and collecting sufficient measurements to reach the goal state with a given confidence level. We present a robust perception-aware bi-directional A* planner for differentially flat systems such as a unicycle and a quadrotor UAV and use a derivative-free Kalman filter to approximate the belief dynamics in the flat space. We also propose an efficient way of ensuring continuity and feasibility by exploiting the convex-hull property of B-spline curves.