Perception-driven control strategy for bio-intelligent cyborg insect

We developed a non-invasive ultraviolet (UV) stimulation method to control the movement of a bio-intelligent cyborg insect by utilizing its natural sensory and motor pathways. This approach allowed the insect to retain its own decision-making ability while its movement direction could be guided. However, the control relied mainly on body motion data, making it difficult to understand how the insect perceived its environment. In this study, we investigate the relationship between physiological data and behavioral data during insect perception and propose a perception-driven control strategy. The proposed method combines insect physiological data, including low-frequency neural amplitude features and heartbeat activity, together with body motion data to estimate the insect’s environment-associated internal perception using machine learning under different environmental conditions, such as natural, UV, chemical, heat, and food. The inferred environment-associated internal perception is used within a closed-loop bio-intelligent cyborg insect control strategy to modulate its behavior. The results show that physiological data and behavioral data are linked to the insect’s environment-associated internal perception, and that perception-driven estimation can improve movement control, demonstrating the potential of the perception-driven control strategy for bio-intelligent cyborg insects in low-power robotic applications.