Autonomous robots are increasingly prevalent in our society, emerging in medical care, transportation vehicles, and home assistance. These robots rely on motion planning and collision detection to identify a sequence of movements allowing them to navigate to an end goal without colliding with the surrounding environment. While many specialized accelerators have been proposed to meet the real-time requirements of robotics planning tasks, they often lack the flexibility to adapt to the rapidly changing landscape of robotics and support future advancements. However, GPUs are well-positioned for robotics and we find that they can also tackle collision detection algorithms with enhancements to existing ray tracing accelerator (RTA) units. Unlike intersection tests in ray tracing, collision queries in robotics require control flow mechanisms to avoid unnecessary computations in each query. In this work, we explore and compare different architectural modifications to address the gaps of existing GPU RTAs. Our proposed RoboGPU architecture introduces a RoboCore that computes collision queries 3.1$\times$ faster than RTA implementations and 14.8$\times$ faster than a CUDA baseline. RoboCore is also useful for other robotics tasks, achieving 3.6$\times$ speedup on a state-of-the-art neural motion planner and 1.1$\times$ speedup on Monte Carlo Localization compared to a baseline GPU. RoboGPU matches the performance of dedicated hardware accelerators while being able to adapt to evolving motion planning algorithms and support classical algorithms.

翻译：自主机器人在我们的社会中日益普及，已广泛应用于医疗护理、交通工具和家庭辅助等领域。这些机器人依赖运动规划与碰撞检测来识别一系列运动序列，使其能够在避免与周围环境发生碰撞的前提下导航至最终目标。尽管已有许多专用加速器被提出以满足机器人规划任务的实时性需求，但它们往往缺乏灵活性，难以适应机器人学领域的快速变革并支持未来技术发展。然而，GPU在机器人学应用中具有显著优势，我们发现通过增强现有光线追踪加速器单元，GPU同样能够高效处理碰撞检测算法。与光线追踪中的相交测试不同，机器人学中的碰撞查询需要控制流机制以避免每次查询中的不必要计算。本研究探索并比较了不同的架构改进方案以弥补现有GPU光线追踪加速器的不足。我们提出的RoboGPU架构引入了RoboCore模块，其碰撞查询计算速度比光线追踪加速器实现快3.1倍，比CUDA基准实现快14.8倍。RoboCore模块对其他机器人学任务同样具有实用价值：在最先进的神经运动规划器上实现3.6倍加速，在蒙特卡洛定位任务中相比基准GPU实现1.1倍加速。RoboGPU在保持与专用硬件加速器相当性能的同时，能够适应不断演进的运动规划算法并支持经典算法。