Bio-inspired robotic locomotion model: Response towards food gradient changes and temperature variation - Kamarudin, Muhammad Raihaan; Mat Ibrahim, Masrullizam; Zainudin, Muhammad Noorazlan Shah; Ramlee, Radi Husin (Taylor's University, 2022)
The nervous system is a complex yet efficient structure - with superior information processing capabilities that surely surpass any man-made high-performance computer. Understanding this technology and utilising it in robotic navigation applications is essential to understand its underlying mechanism. One of the approaches is using a nematode’s biological network model, as having a simple network structure while holding a complex locomotion behaviour. For instance, its ability to navigate via local concentration cue (chemotaxis) and the ability to dynamically respond towards surrounding temperature (thermotaxis). To date, the simulation of currently available models is on static environment conditions and the nematode’s movement decision is based on the deterministic non-linear response towards gradient changes. Commonly, parameters of these models were optimised based on static conditions and require adjustment if simulated within a dynamic environment. Therefore, this work proposed a new nematode’s biological locomotion model where the movement trajectory is determined by the probability of “Run” and “Turn” signals. The model is simulated within a 2D virtual environment with complex concentration gradient and variants of temperature distribution. The analysis result shows the nematode’s movement of the proposed model agreed with the finding from experimental studies. Later, the proposed model in this work will be employed to develop a biological inspired multi-sensory robotic system for navigating within a dynamic and complex environment