DaSP-RRT: Data-Driven Safe Performance-Aware Motion Planning
Nariman Niknejad, Ramin Esmzad, Teawon Han, Gokul Siva Sankar, Hamidreza Modares · IEEE Robotics and Automation Letters · 2025
This letter presents a data-driven safe motion planning approach,DaSP-RRT, designed to generate collision-free paths with guaranteed optimality through the use of invariant sets. The proposed planner constructs a sequence of performance-aware invariant sets using available data and a new control design approach. These sets are centered around randomly generated waypoints, which are then connected to form a continuous path from the initial to the target point. For each waypoint, an optimization problem determines the largest performance-aware invariant set and learns its corresponding controller. A key feature of the algorithm is its incorporation of performance-reachability between connected waypoints, leveraging available resources and system information to minimize the need for frequent re-planning. The effectiveness ofDaSP-RRTis demonstrated through a real-world implementation on an omnidirectional wheeled robot and simulations on spacecraft motion planning. These scenarios, which include obstacle avoidance, highlight the algorithm's potential for practical, real-world applications.