Integrating contact-awareness into a soft snake robot and efficiently controlling its locomotion in response to contact information present significant challenges. This paper aims to solve contact-aware locomotion problem of a soft snake robot through 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' \textit{scale sensilla}. In the design of control framework, our core contribution is the development of a novel sensory feedback mechanism of the Matsuoka central pattern generator (CPG) network. This mechanism allows the Matsuoka CPG system to work like a "spine 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 generate rhythmic signals to effectively actuate the soft snake robot to slither through densely allocated obstacles. In the design of the "reflex arc", we develop two 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 special topology. These two reactive controllers respectively facilitate two different contact-aware locomotion control schemes. The two control schemes are tested and evaluated in the soft snake robot, showing promising performance in the contact-aware locomotion tasks. The experimental results also further verify the benefit of Matsuoka CPG system in bio-inspired robot controller design.

翻译：将接触感知能力集成到软体蛇形机器人中，并利用接触信息高效控制其运动，是一项重大挑战。本文旨在通过开发仿生接触感知运动控制器来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天然蛇类鳞片感受器的鳞片覆盖式传感器结构。在控制框架设计中，我们的核心贡献是提出了一种新型的Matsuoka中枢模式发生器（CPG）网络感觉反馈机制。该机制使Matsuoka CPG系统在接触感知控制方案中如同"脊髓"般工作，可同时接收来自"大脑"（目标追踪运动控制器）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，生成节律性信号以有效驱动软体蛇形机器人穿越密集障碍物。在"反射弧"的设计中，我们开发了两类反应控制器：1）强化学习（RL）传感器调节器，学习操控CPG系统的感觉反馈输入；2）局部反射式传感器-CPG网络，以特殊拓扑结构将传感器读数与CPG反馈输入直接连接。这两类控制器分别实现了两种不同的接触感知运动控制方案。两种控制方案在软体蛇形机器人上进行了测试评估，在接触感知运动任务中展现出优异性能。实验结果进一步验证了Matsuoka CPG系统在仿生机器人控制器设计中的优势。