Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA 94720, USA.
Sci Robot. 2020 Nov 18;5(48). doi: 10.1126/scirobotics.abd7710.
Robots for picking in e-commerce warehouses require rapid computing of efficient and smooth robot arm motions between varying configurations. Recent results integrate grasp analysis with arm motion planning to compute optimal smooth arm motions; however, computation times on the order of tens of seconds dominate motion times. Recent advances in deep learning allow neural networks to quickly compute these motions; however, they lack the precision required to produce kinematically and dynamically feasible motions. While infeasible, the network-computed motions approximate the optimized results. The proposed method warm starts the optimization process by using the approximate motions as a starting point from which the optimizing motion planner refines to an optimized and feasible motion with few iterations. In experiments, the proposed deep learning-based warm-started optimizing motion planner reduces compute and motion time when compared to a sampling-based asymptotically optimal motion planner and an optimizing motion planner. When applied to grasp-optimized motion planning, the results suggest that deep learning can reduce the computation time by two orders of magnitude (300×), from 29 s to 80 ms, making it practical for e-commerce warehouse picking.
电子商务仓库中的拣选机器人需要快速计算不同配置之间高效、平滑的机械臂运动。最近的研究成果将抓取分析与机械臂运动规划相结合，以计算出最优的平滑机械臂运动；然而，其计算时间通常需要数十秒，这大大超过了运动时间。深度学习的最新进展使得神经网络能够快速计算这些运动；然而，它们缺乏生成运动学和动力学可行运动所需的精度。虽然不可行，但网络计算出的运动接近优化结果。该方法通过使用近似运动作为起点，从而快速启动优化过程，该起点是优化运动规划器在很少的迭代次数内细化到优化和可行运动的起点。在实验中，与基于采样的渐近最优运动规划器和优化运动规划器相比，基于深度学习的带预热的优化运动规划器可以减少计算和运动时间。当应用于抓取优化的运动规划时，结果表明深度学习可以将计算时间减少两个数量级（300 倍），从 29 秒减少到 80 毫秒，这使得它在电子商务仓库拣选方面具有实用性。
