Bio-Inspired Distributed Neural Locomotion Controller (D-NLC) for Robust Locomotion and Emergent Behaviors

Despite having fewer neurons than more complex life forms, insects are still capable of producing astonishing locomotive behaviors, such as traversing diverse environments and making rapid gait adaptations after extreme injury or autotomy. Biologists attribute this to a chain of segmental neuron clusters (ganglia) within insect nervous systems, which act as distributed self-organizing sensorimotor control units. Inspired by the neural structure of the Carausius morosus, the common stick insect, this work introduces the Distributed Neural Locomotion Controller (D-NLC), a modular control framework that utilizes local proprioceptive feedback to modulate joint-level Central Pattern Generator (CPG) signals to produce emergent locomotive behaviors. This framework was implemented on a modular legged robot with distributed jointlevel embedded computing units. In addition, assessments were conducted on the framework's performance and behavior in various experimental settings. Based on real-world experiments, we observe an overall 31.3% average increase in curvilinear motion performance under external (terrain) and internal (amputation) perturbation compared to a centralized predefined gait controller. This difference is statistically significant $(P \ll 0.05)$ for larger perturbations but not for single-leg amputations. Experiments with perturbation-induced leg stance duration and leg phase-difference analysis further validated our hypothesis regarding D-NLC's role in the robust perceptive locomotion and self-emergent gait adaptation against complex unforeseen perturbations. This proposed control framework does not require any numerical optimization or weight training processes, which are time-consuming and computationally expensive. To the best of our knowledge, this framework is the first bio-inspired neural controller deployed on a distributed embedded system. More info at https://eigenbot-dnlc.github.io.