用GPU实时编译加速复杂约束下的机器人路径规划。
cpRRTC: GPU-Parallel RRT-Connect for Constrained Motion Planning
- 基于NVRTC实现运行时编译，动态优化路径规划计算。
- 在复杂机器人模型和环境上，规划速度优于现有方法。
- 适合需要高实时性与复杂场景的机器人应用开发。
运动规划是机器人领域的基础问题，旨在生成机器人可执行的可行轨迹。近年来，通过CPU和GPU等并行计算架构，规划时间已降至毫秒级。然而，基于采样的约束运动规划在GPU上的研究仍不充分。先前工作如pRRTC利用追踪编译器与CUDA后端加速正向运动学和碰撞检测，在简单场景中表现良好，但在机器人模型或环境复杂度增加时性能下降。本文提出一种新型基于GPU的框架，采用NVRTC进行运行时编译，有效处理高复杂度场景，支持约束运动规划。实验表明，该方法在多种复杂场景下均显著优于现有方法。
原文摘要 · Abstract (English)
Motion planning is a fundamental problem in robotics that involves generating feasible trajectories for a robot to follow. Recent advances in parallel computing, particularly through CPU and GPU architectures, have significantly reduced planning times to the order of milliseconds. However, constrained motion planning especially using sampling based methods on GPUs remains underexplored. Prior work such as pRRTC leverages a tracking compiler with a CUDA backend to accelerate forward kinematics and collision checking. While effective in simple settings, their approach struggles with increased complexity in robot models or environments. In this paper, we propose a novel GPU based framework utilizing NVRTC for runtime compilation, enabling efficient handling of high complexity scenarios and supporting constrained motion planning. Experimental results demonstrate that our method achieves superior performance compared to existing approaches.
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