This paper aims to solve the contact-aware locomotion problem of a soft snake robot by developing bio-inspired contact-aware locomotion controllers. To provide effective contact information for the controllers, we develop a scale-covered sensor structure mimicking natural snakes' scale sensilla. In the design of the control framework, our core contribution is the development of a novel sensory feedback mechanism for the Matsuoka central pattern generator (CPG) network. This mechanism allows the Matsuoka CPG system to work like a "spinal cord" in the whole contact-aware control scheme, which simultaneously takes the stimuli including tonic input signals from the "brain" (a goal-tracking locomotion controller) and sensory feedback signals from the "reflex arc" (the contact reactive controller), and generates rhythmic signals to actuate the soft snake robot to slither through densely allocated obstacles. In the "reflex arc" design, we develop two distinctive types of reactive controllers -- 1) a reinforcement learning (RL) sensor regulator that learns to manipulate the sensory feedback inputs of the CPG system, and 2) a local reflexive sensor-CPG network that directly connects sensor readings and the CPG's feedback inputs in a specific topology. Combining with the locomotion controller and the Matsuoka CPG system, these two reactive controllers facilitate two different contact-aware locomotion control schemes. The two control schemes are tested and evaluated in both simulated and real soft snake robots, showing promising performance in the contact-aware locomotion tasks. The experimental results also validate the advantages of the modified Matsuoka CPG system with a new sensory feedback mechanism for bio-inspired robot controller design.

翻译：本文旨在通过开发仿生接触感知运动控制器，解决软体蛇形机器人的接触感知运动问题。为向控制器提供有效的接触信息，我们开发了一种模拟自然蛇类鳞片感受器的鳞片覆盖传感器结构。在控制框架设计中，我们的核心贡献是为松冈中央模式发生器（CPG）网络开发了一种新型感觉反馈机制。该机制使松冈CPG系统在整体接触感知控制方案中发挥"脊髓"作用，能同时接收来自"大脑"（目标跟踪运动控制器）的强直输入信号和来自"反射弧"（接触反应控制器）的感觉反馈信号，并生成节律信号驱动软体蛇形机器人穿越密集障碍物。在"反射弧"设计中，我们开发了两种独特的反应控制器：1) 强化学习（RL）传感器调节器——通过学习调控CPG系统的感觉反馈输入；2) 局部反射式传感器-CPG网络——以特定拓扑结构直接连接传感器读数与CPG反馈输入。结合运动控制器与松冈CPG系统，这两种反应控制器构成了两种不同的接触感知运动控制方案。在仿真和实际软体蛇形机器人上的测试评估表明，这两种方案在接触感知运动任务中展现出良好性能。实验结果同时验证了具有新型感觉反馈机制的改进型松冈CPG系统在仿生机器人控制器设计中的优势。