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.Comment: 17 pages, 19 figure