RRTX: Real-time motion planning/replanning for environments with unpredictable obstacles

Abstract

We present RRTX, the first asymptotically optimal sampling-based motion planning algorithm for real-time navigation in dynamic environments (containing obstacles that unpredictably appear, disappear, andmove). Whenever obstacle changes are observed, e.g., by onboard sensors, a graph rewiring cascade quickly updates the search-graph and repairs its shortest-path-to-goal subtree. Both graph and tree are built directly in the robot’s state space, respect the kinematics of the robot, and continue to improve during navigation. RRTX is also competitive in static environments—where it has the same amortized per iteration runtime as RRT and RRT* Θ (log n) and is faster than RRT# ω (log2 n). In order to achieve O (log n) iteration time, each node maintains a set of O (log n) expected neighbors, and the search graph maintains ϵ-consistency for a predefined ϵ.