SRMP: Search-Based Robot Motion Planning Library

2025-09-29

AI Summary

Existing motion planning frameworks struggle to simultaneously ensure trajectory predictability, cross-platform consistency, and hardware deployability in industrial-grade safety-critical, high-repetition scenarios—such as robotic learning dataset construction and multi-robot coordination. To address this, we propose the first open-source motion planning framework specifically designed for multi-robot manipulation tasks. It integrates search-based algorithmic design, supports major simulators including MuJoCo, SAPIEN, and PyBullet, and provides dual Python/C++ APIs alongside a MoveIt! plugin for seamless hardware integration. Our framework achieves unprecedented reproducibility and cross-platform consistency in trajectory generation for multi-robot tasks. Extensive validation across diverse robot platforms demonstrates significant improvements in planning stability, inter-platform consistency, and deployment efficiency—thereby bridging a critical gap in reliable, safety-aware motion planning tools for real-world industrial applications.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Motion planning is a critical component in any robotic system. Over the years, powerful tools like the Open Motion Planning Library (OMPL) have been developed, offering numerous motion planning algorithms. However, existing frameworks often struggle to deliver the level of predictability and repeatability demanded by high-stakes applications -- ranging from ensuring safety in industrial environments to the creation of high-quality motion datasets for robot learning. Complementing existing tools, we introduce SRMP (Search-based Robot Motion Planning), a new software framework tailored for robotic manipulation. SRMP distinguishes itself by generating consistent and reliable trajectories, and is the first software tool to offer motion planning algorithms for multi-robot manipulation tasks. SRMP easily integrates with major simulators, including MuJoCo, Sapien, Genesis, and PyBullet via a Python and C++ API. SRMP includes a dedicated MoveIt! plugin that enables immediate deployment on robot hardware and seamless integration with existing pipelines. Through extensive evaluations, we demonstrate in this paper that SRMP not only meets the rigorous demands of industrial and safety-critical applications but also sets a new standard for consistency in motion planning across diverse robotic systems. Visit srmp.readthedocs.io for SRMP documentation and tutorials.

Problem

• Ensuring predictable motion planning for safety-critical applications
• Generating consistent trajectories for robotic manipulation tasks
• Providing multi-robot motion planning algorithms for manipulation

Innovation

• A search-based framework for robotic manipulation
• Generates consistent and reliable motion trajectories
• First tool with multi-robot manipulation planning algorithms