A decoupled Bayesian method for snake robot control in unstructured environment

This paper presents a method which avoids the common practice of using a complex coupled snake robot model and performing kinematic analysis for control in cluttered environments. Instead, we introduce a completely decoupled dynamical Bayesian formulation with respect to interacted snake robot links and environmental objects, which requires much lower complexity for efficient and robust control. When a snake robot does not interact with obstacles, it runs by a simple serpenoid controller. However, when it exhibits interaction with environments, defined as close proximity or collision with targets and/or obstacles, we extend the conventional Bayesian framework by modeling such interactions in terms of stimuli. The proposed 'multi-neural-stimulus function' represents the cumulative effect of both external environmental influences and internal constraints of the snake robot. It implicitly handles the 'unexpected collision' problem and thus solve the difficult data association and shape adjustment problems for snake robot control in an innovative way. Preliminary experimental results have demonstrated promising performance of the proposed method comparing with the state-of-the-art.