A CPG-Based Gait Controller for a Soft Crawling Robot

Gait control in soft crawling robots is commonly based on manually designed control rules, which often results in limited adaptability, high parameter dependency, and unsmooth transitions between locomotion modes. These limitations hinder the robot's ability to operate effectively in unstructured or dynamic environments. To address this, we propose a biologically inspired gait control strategy for the designed soft crawling robot, based on a central pattern generator (CPG) network composed of Hopfield oscillators. Each oscillator is mapped to an actuator of the soft robot. The CPG network autonomously generates rhythmic control signals, which are transformed into pulse-width modulation (PWM) signals through a discretization method. Experimental validation is conducted across multiple locomotion scenarios including forward crawling, turning, and rolling. The results show that the gait controller enables fast convergence from arbitrary initial states, smooth gait transitions through parameter modulation, demonstrating a scalable and effective solution for generating adaptive and continuous locomotion patterns in soft crawling robots.