Liu Mei, He Li, Hu Bin, Li Shuai
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Neural Netw. 2021 Jun;138:164-178. doi: 10.1016/j.neunet.2021.02.002. Epub 2021 Feb 17.
Recurrent neural network (RNN), as a kind of neural network with outstanding computing capability, improvability, and hardware realizability, has been widely used in various fields, especially in robotics. In this paper, an RNN with noise rejection is deliberately constructed to remedy the issue of joint-angle drift frequently occurring during the cyclic motion generation (CMG) of a manipulator in a noisy environment. Different from general RNNs, the proposed RNN possesses inherent noise immunity, especially for time-varying polynomial noises. Besides, proofs on the convergence of the proposed RNN in the absence and presence of noises are given. Furthermore, we carry out simulations on manipulators PUMA 560 and UR5 to demonstrate the reliability of the proposed RNN in remedying joint-angle drift, and comparison simulations under different noisy conditions further verify its superiority. In addition, experiments are conducted on manipulator FRANKA Panda to elucidate the realizability of the proposed RNN.
循环神经网络（RNN）作为一种具有出色计算能力、可改进性和硬件可实现性的神经网络，已在各个领域得到广泛应用，尤其是在机器人技术中。本文特意构建了一种具有噪声抑制功能的RNN，以解决在有噪声环境下机械手循环运动生成（CMG）过程中频繁出现的关节角度漂移问题。与一般的RNN不同，所提出的RNN具有固有的抗噪声能力，特别是对于时变多项式噪声。此外，还给出了所提出的RNN在无噪声和有噪声情况下的收敛性证明。此外，我们在机械手PUMA 560和UR5上进行了仿真，以证明所提出的RNN在纠正关节角度漂移方面的可靠性，不同噪声条件下的对比仿真进一步验证了其优越性。此外，还在机械手FRANKA Panda上进行了实验，以阐明所提出的RNN的可实现性。
