Sun Tao, Xiong Xiaofeng, Dai Zhendong, Owaki Dai, Manoonpong Poramate
Institute of Bio-inspired Structure and Surface Engineering, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.
Front Robot AI. 2021 Apr 12;8:638684. doi: 10.3389/frobt.2021.638684. eCollection 2021.
Walking animals demonstrate impressive self-organized locomotion and adaptation to body property changes by skillfully manipulating their complicated and redundant musculoskeletal systems. Adaptive interlimb coordination plays a crucial role in this achievement. It has been identified that interlimb coordination is generated through dynamical interactions between the neural system, musculoskeletal system, and environment. Based on this principle, two classical interlimb coordination mechanisms (continuous phase modulation and phase resetting) have been proposed independently. These mechanisms use decoupled central pattern generators (CPGs) with sensory feedback, such as ground reaction forces (GRFs), to generate robot locomotion autonomously without predefining it (i.e., self-organized locomotion). A comparative study was conducted on the two mechanisms under decoupled CPG-based control implemented on a quadruped robot in simulation. Their characteristics were compared by observing their CPG phase convergence processes at different control parameter values. Additionally, the mechanisms were investigated when the robot faced various unexpected situations, such as noisy feedback, leg motor damage, and carrying a load. The comparative study reveals that the phase modulation and resetting mechanisms demonstrate satisfactory performance when they are subjected to symmetric and asymmetric GRF distributions, respectively. This work also suggests a strategy for the appropriate selection of adaptive interlimb coordination mechanisms under different conditions and for the optimal setting of their control parameter values to enhance their control performance.
行走的动物通过巧妙地操控其复杂且冗余的肌肉骨骼系统，展现出令人印象深刻的自组织运动能力以及对身体属性变化的适应性。适应性肢体间协调在这一过程中起着至关重要的作用。据已确定，肢体间协调是通过神经系统、肌肉骨骼系统和环境之间的动态相互作用产生的。基于这一原理，两种经典的肢体间协调机制（连续相位调制和相位重置）已被分别独立提出。这些机制使用具有感觉反馈（如地面反作用力（GRF））的解耦中枢模式发生器（CPG），在不预先定义的情况下自主生成机器人运动（即自组织运动）。在模拟环境中，对基于解耦CPG控制的四足机器人上实现的这两种机制进行了对比研究。通过观察它们在不同控制参数值下的CPG相位收敛过程来比较其特性。此外，还研究了机器人面对各种意外情况（如噪声反馈、腿部电机损坏和负载）时的这些机制。对比研究表明，相位调制和重置机制在分别面对对称和非对称GRF分布时表现出令人满意的性能。这项工作还提出了一种策略，用于在不同条件下适当选择适应性肢体间协调机制，并对其控制参数值进行优化设置，以提高其控制性能。
