  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.
