pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning

2025-03-09

AI Summary

RRT-Connect suffers from high computational cost, expensive collision checking, and limited parallelization—typically restricted to isolated modules or independent instances—in high-dimensional robot motion planning. Method: We propose the first end-to-end, full-algorithm GPU-accelerated RRT-Connect implementation. It integrates multi-threaded tree expansion with bidirectional connection, SIMT-optimized high-accuracy collision detection, and a hierarchical parallel strategy coordinating expansion, connection, and collision checking. Contribution/Results: Evaluated on MotionBenchMaker, our method achieves 6× average speedup, 5× reduction in solution-time variance, and 1.5× improvement in initial path cost for 7–14 DOF robotic arms navigating complex environments. This demonstrates substantial gains in planning efficiency, robustness, and path quality over prior approaches.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Sampling-based motion planning algorithms, like the Rapidly-Exploring Random Tree (RRT) and its widely used variant, RRT-Connect, provide efficient solutions for high-dimensional planning problems faced by real-world robots. However, these methods remain computationally intensive, particularly in complex environments that require many collision checks. As such, to improve performance, recent efforts have explored parallelizing specific components of RRT, such as collision checking or running multiple planners independently, but no prior work has integrated parallelism at multiple levels of the algorithm for robotic manipulation. In this work, we present pRRTC, a GPU-accelerated implementation of RRT-Connect that achieves parallelism across the entire algorithm through multithreaded expansion and connection, SIMT-optimized collision checking, and hierarchical parallelism optimization, improving efficiency, consistency, and initial solution cost. We evaluate the effectiveness of pRRTC on the MotionBenchMaker dataset using robots with 7, 8, and 14 degrees-of-freedom, demonstrating up to 6x average speedup on constrained reaching tasks at high collision checking resolution compared to state-of-the-art. pRRTC also demonstrates a 5x reduction in solution time variance and 1.5x improvement in initial path costs compared to state-of-the-art motion planners in complex environments across all robots.

Problem

• Accelerates motion planning using GPU-parallel RRT-Connect.
• Reduces computational cost and improves solution consistency.
• Enhances performance in complex, high-dimensional environments.

Innovation

• GPU-accelerated RRT-Connect for motion planning
• Multithreaded expansion and connection optimization
• SIMT-optimized collision checking for efficiency