Search-Based Robot Motion Planning With Distance-Based Adaptive Motion Primitives

2025-07-01

AI Summary

To address the inefficiency of fixed-size motion primitives in high-degree-of-freedom robotic arm planning within complex environments, this paper proposes adaptive motion primitives—variable-radius *burs* (ball-based motion primitives) in configuration space—integrating sampling-based and search-based paradigms into a unified graph-search planning framework. Leveraging the SMPL library, burs are dynamically generated and collision-checked, with their radii adaptively scaled to local free-space geometry. Experiments demonstrate that the method significantly reduces the number of search nodes and planning time, outperforming fixed-radius primitives in high-dimensional, cluttered scenarios while maintaining robustness and efficiency in simpler environments. The core contribution is the first introduction of variable-scale burs as motion primitives within a cohesive planning framework, effectively balancing exploration efficiency and path quality.

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

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This work proposes a motion planning algorithm for robotic manipulators that combines sampling-based and search-based planning methods. The core contribution of the proposed approach is the usage of burs of free configuration space (C-space) as adaptive motion primitives within the graph search algorithm. Due to their feature to adaptively expand in free C-space, burs enable more efficient exploration of the configuration space compared to fixed-sized motion primitives, significantly reducing the time to find a valid path and the number of required expansions. The algorithm is implemented within the existing SMPL (Search-Based Motion Planning Library) library and evaluated through a series of different scenarios involving manipulators with varying number of degrees-of-freedom (DoF) and environment complexity. Results demonstrate that the bur-based approach outperforms fixed-primitive planning in complex scenarios, particularly for high DoF manipulators, while achieving comparable performance in simpler scenarios.

Problem

• Enhances robotic motion planning efficiency with adaptive primitives
• Reduces pathfinding time in complex high-DoF manipulator scenarios
• Improves C-space exploration using free-space adaptive motion bursts

Innovation

• Combines sampling and search-based planning methods
• Uses adaptive motion primitives in free C-space
• Implements bur-based approach in SMPL library