一种用于提高冗余机器人运动规划完备性的新型循环神经网络。
A Novel Recurrent Neural Network for Improving Redundant Manipulator Motion Planning Completeness.
作者信息
Li Yangming, Li Shuai, Hannaford Blake
机构信息
Department of Electrical Engineering, University of Washington, Seattle, WA, USA 98195.
Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
出版信息
IEEE Int Conf Robot Autom. 2018 May;2018:2956-2961. doi: 10.1109/icra.2018.8461204. Epub 2018 Sep 13.
Recurrent Neural Networks (RNNs) demonstrated advantages on control precision, system robustness and computational efficiency, and have been widely applied to redundant manipulator control optimization. Existing RNN control schemes locally optimize trajectories and are efficient and reliable on obstacle avoidance. However, for motion planning, they suffer from local minimum and do not have planning completeness. This work explained the cause of the planning incompleteness and addressed the problem with a novel RNN control scheme. The paper presented the proposed method in detail and analyzed the global stability and the planning completeness in theory. The proposed method was compared with other three control schemes on the precision, the robustness and the planning completeness in software simulation and the results shows the proposed method has improved precision and robustness, and planning completeness.
递归神经网络（RNN）在控制精度、系统鲁棒性和计算效率方面展现出优势，并已广泛应用于冗余机器人控制优化。现有的RNN控制方案可对轨迹进行局部优化，在避障方面高效且可靠。然而，对于运动规划而言，它们存在局部极小值问题且不具备规划完备性。这项工作解释了规划不完备性的原因，并采用一种新颖的RNN控制方案解决了该问题。本文详细介绍了所提出的方法，并从理论上分析了全局稳定性和规划完备性。在软件仿真中，将所提出的方法与其他三种控制方案在精度、鲁棒性和规划完备性方面进行了比较，结果表明所提出的方法在精度、鲁棒性和规划完备性方面均有提升。
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