BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
This paper addresses the need for efficient, safe motion planning under kinodynamic constraints for robots in cluttered environments, offering a scalable solution with theoretical convergence guarantees.
The BOW Planner uses constrained Bayesian optimization over a planning window of reachable velocities to achieve rapid, safe motion planning in complex environments, demonstrating substantial improvements in computation times, trajectory lengths, and solution times over existing methods.
This paper introduces the BOW Planner, a scalable motion planning algorithm designed to navigate robots through complex environments using constrained Bayesian optimization (CBO). Unlike traditional methods, which often struggle with kinodynamic constraints such as velocity and acceleration limits, the BOW Planner excels by concentrating on a planning window of reachable velocities and employing CBO to sample control inputs efficiently. This approach enables the planner to manage high-dimensional objective functions and stringent safety constraints with minimal sampling, ensuring rapid and secure trajectory generation. Theoretical analysis confirms the algorithm's asymptotic convergence to near-optimal solutions, while extensive evaluations in cluttered and constrained settings reveal substantial improvements in computation times, trajectory lengths, and solution times compared to existing techniques. Successfully deployed across various real-world robotic systems, the BOW Planner demonstrates its practical significance through exceptional sample efficiency, safety-aware optimization, and rapid planning capabilities, making it a valuable tool for advancing robotic applications. The BOW Planner is released as an open-source package and videos of real-world and simulated experiments are available at https://bow-web.github.io.