Disorder and Homeostasis in ANIBOT A Biologically-Inspired Animal Robot

Area of Science:

• Neuroscience
• Robotics
• Complex Systems

Background:

• Central pattern generators (CPGs) are neural circuits controlling rhythmic behaviors like locomotion.
• ANIBOT is a four-legged robot inspired by biological systems, utilizing a CPG model.
• Homeostasis, the ability to maintain stable internal conditions, is crucial for biological and artificial systems.

Purpose of the Study:

• To investigate the impact of external perturbations on CPG networks in the context of homeostasis.
• To analyze the resilience of ANIBOT's locomotion patterns to network disorder.
• To explore the phenomenon of disorder-induced pattern formation in neural networks.

Main Methods:

• Analytical and computational modeling of CPG networks.
• Application of mathematical frameworks for homeostasis, including input-output functions.
• Simulation of perturbations including network connectivity changes, internal dynamics, and electronic noise.

Main Results:

• CPG phase dynamics control ANIBOT's locomotion patterns (Walk, Jump, Trot, Bound, Pace, Pronk).
• Except for Walk and Jump, most gaits demonstrated perfect homeostatic responses to perturbations.
• External perturbations can induce novel locomotion patterns not present in the unperturbed system.

Conclusions:

• CPG networks exhibit significant homeostatic capabilities in maintaining locomotion patterns against external disturbances.
• Disorder-induced pattern formation is a notable characteristic of these perturbed CPG networks.
• Findings have implications for designing robust bio-inspired robots and understanding neural control.