Mitigating Neural Habituation in Insect Bio-Bots: A Dual-Timescale Adaptive Control Approach

Bio-cybernetic organisms combine biological locomotion with electronic control but face significant challenges regarding individual variability and stimulus habituation. This study introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) designed to dynamically calibrate to individual Gromphadorhina portentosa specimens. Using a miniaturized neural controller, we compared ANFIS's performance against natural behavior and non-adaptive control methods. Results demonstrate ANFIS's superiority: obstacle navigation efficiency reached 81% (compared to 42% for non-adaptive methods), and effective behavioral modulation was sustained for 47 min (versus 26 min). Furthermore, the system achieved 73% target acquisition in complex terrain and maintained stimulus responsiveness 3.5-fold longer through sophisticated habituation compensation. Biocompatibility assessments confirmed interface functionality over 14-day periods. This research establishes foundational benchmarks for arthropod bio-cybernetics, demonstrating that adaptive neuro-fuzzy architectures significantly outperform conventional methods, enabling robust bio-hybrid platforms suitable for confined-space search-and-rescue operations.