Reactive motion generation in unstructured environments remains an open challenge in robotics. Due to the computational complexity of collision-free motion generation, existing methods either generate global trajectories for static scenarios, or employ models that make conservative assumptions about the environment. This paper identifies the primary bottleneck as the runtime performance demand of planning on high-fidelity environments, and the temporal integration between the perception and planning modules. Therefore, we propose a framework that does not compromise on runtime performance and world representations for perception and planning by accelerating world modeling and vector-field based planning using the GPU. This allows us to achieve faster parallel state exploration for quasi-global trajectory planning, and tighter coupling of the perception-action loop in real-time for dynamic cluttered environments with off-the-shelf depth sensors. We quantitatively evaluate the computation-time and success rate differences for the CPU and GPU versions of our planner, and perform qualitative evaluations of our coupled framework using real-world experiments on a 7-DoF Franka Emika robot. Experimental results demonstrate that our GPU-based framework achieves up to a 5x speedup over the CPU version and successfully avoids collisions across both trivial and challenging physical world scenarios.

翻译：在非结构化环境中的反应式运动生成仍是机器人领域的一个开放性挑战。由于无碰撞运动生成的计算复杂度，现有方法要么针对静态场景生成全局轨迹，要么采用对环境做出保守假设的模型。本文指出，主要瓶颈在于高保真环境对规划运行的性能需求，以及感知与规划模块之间的时间集成问题。为此，我们提出一种框架，通过使用GPU加速世界建模和基于向量场的规划，在不牺牲运行性能和世界表示的前提下实现感知与规划。这使得我们能够实现准全局轨迹规划的更快并行状态探索，并利用现成的深度传感器在动态杂乱环境中实时实现感知-动作环的紧耦合。我们定量评估了规划器CPU版本与GPU版本的计算时间和成功率差异，并通过在7自由度Franka Emika机器人上的真实世界实验对所提出的耦合框架进行了定性评估。实验结果表明，基于GPU的框架相较于CPU版本实现了高达5倍的加速，且在简单和复杂的物理世界场景中均能成功避免碰撞。