This article demonstrates that collision detection-intensive applications such as robotic motion planning may be accelerated by performing collision checks with a machine learning model. We propose Fastron, a learning-based algorithm, to model a robot's configuration space to be used as a proxy collision detector in place of standard geometric collision checkers. We demonstrate that leveraging the proxy collision detector results in up to an order of magnitude faster performance in robot simulation and planning than state-of-the-art collision detection libraries. Our results show that Fastron learns a model more than 100 times faster than a competing C-space modeling approach, while also providing theoretical guarantees of learning convergence. Using the open motion planning libraries (OMPLs), we were able to generate initial motion plans across all experiments with varying robot and environment complexities and workspace obstacle locations. With Fastron, we can repeatedly generate new motion plans at a 56 Hz rate, showing its application toward autonomous surgical assistance task in shared environments with human-controlled manipulators. All performance gains were achieved despite using only CPU-based calculations, suggesting further computational gains with a GPU approach that can parallelize tensor algebra. Code is available online.1

中文翻译：

---

用于机器人运动规划应用的基于学习的代理碰撞检测

本文展示了可以通过使用机器学习模型执行碰撞检查来加速碰撞检测密集型应用程序，例如机器人运动规划。我们提出了 Fastron，一种基于学习的算法，对机器人的配置空间进行建模，以用作代理碰撞检测器代替标准几何碰撞检查器。我们证明，与最先进的碰撞检测库相比，利用代理碰撞检测器可以使机器人模拟和规划的性能提高一个数量级。我们的结果表明 Fastron 学习模型的速度比竞争的 C 空间建模方法快 100 倍以上，同时还提供了学习收敛的理论保证。使用开放的运动规划库 (OMPL)，我们能够在具有不同机器人和环境复杂性以及工作空间障碍位置的所有实验中生成初始运动计划。使用 Fastron，我们可以以 56 Hz 的频率重复生成新的运动计划，展示其在与人工控制的机械手共享环境中用于自主手术辅助任务的应用。尽管仅使用基于 CPU 的计算，但所有性能提升均已实现，这表明使用可以并行化张量代数的 GPU 方法进一步提高计算性能。代码可在线获取。1 尽管仅使用基于 CPU 的计算，但所有性能提升均已实现，这表明使用可以并行化张量代数的 GPU 方法进一步提高计算性能。代码可在线获取。1 尽管仅使用基于 CPU 的计算，但所有性能提升均已实现，这表明使用可以并行化张量代数的 GPU 方法进一步提高计算性能。代码可在线获取。1