BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments

2025-08-18

AI Summary

This paper addresses the challenge of high-dimensional objective function optimization under stringent dynamical and safety constraints in robot motion planning for complex environments. We propose BOW Planner, a scalable trajectory planning algorithm based on Constrained Bayesian Optimization (CBO). Its key innovation is the “Reachable Velocity Window” mechanism, which restricts control input sampling to a dynamically feasible subspace, substantially reducing the search dimensionality. By integrating CBO modeling with asymptotic convergence guarantees, BOW Planner achieves near-optimal safe trajectories with minimal sample complexity. The algorithm is implemented within an open-source framework and supports real-robot deployment. Experiments across multiple robotic platforms demonstrate that BOW Planner reduces average computation time by 42% and trajectory length by 18% compared to state-of-the-art methods, while significantly improving sample efficiency. The approach thus bridges theoretical rigor with practical engineering applicability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• Efficient motion planning in complex environments with constraints
• Overcoming kinodynamic limitations like velocity and acceleration bounds
• High-dimensional optimization with safety constraints and minimal sampling

Innovation

• Constrained Bayesian optimization for motion planning
• Focus on reachable velocity planning window
• Efficient control input sampling with CBO