Li Qingkai, Pang Yanbo, Wang Yushi, Han Xinyu, Li Qing, Zhao Mingguo
Department of Automation, Tsinghua University, Beijing 100084, China.
Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China.
Biomimetics (Basel). 2023 Aug 25;8(5):389. doi: 10.3390/biomimetics8050389.
Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot's operation. In this paper, inspired by the central nervous system (CNS), we present a CNS-based Biomimetic Motor Control (CBMC) approach consisting of four modules. The first module consists of a cerebellum-like spiking neural network that employs spiking timing-dependent plasticity to learn the dynamics mechanisms and adjust the synapses connecting the spiking neurons. The second module constructed using an artificial neural network, mimicking the regulation ability of the cerebral cortex to the cerebellum in the CNS, learns by reinforcement learning to supervise the cerebellum module with instructive input. The third and last modules are the cerebral sensory module and the spinal cord module, which deal with sensory input and provide modulation to torque commands, respectively. To validate our method, CBMC was applied to the trajectory tracking control of a 7-DoF robotic arm in simulation. Finally, experiments are conducted on the robotic arm using various payloads, and the results of these experiments clearly demonstrate the effectiveness of the proposed methodology.
受脑科学启发，人们提出了许多用于机器人控制的方法，特别是针对那些无法获得动态模型知识的情况。这一点至关重要，因为在机器人运行过程中可能会出现动态模型不准确和变化的情况。在本文中，受中枢神经系统（CNS）的启发，我们提出了一种基于中枢神经系统的仿生运动控制（CBMC）方法，该方法由四个模块组成。第一个模块由一个类似小脑的脉冲神经网络组成，该网络利用脉冲时间依赖可塑性来学习动力学机制，并调整连接脉冲神经元的突触。第二个模块使用人工神经网络构建，模仿中枢神经系统中大脑皮层对小脑的调节能力，通过强化学习进行学习，以指导性输入监督小脑模块。第三个也是最后一个模块是大脑感觉模块和脊髓模块，它们分别处理感觉输入并对扭矩命令提供调制。为了验证我们的方法，在仿真中将CBMC应用于一个7自由度机器人手臂的轨迹跟踪控制。最后，在机器人手臂上使用各种负载进行了实验，这些实验结果清楚地证明了所提出方法的有效性。
