A Bayesian Based Controller for Snake Robot Locomotion in Unstructured Environments
This paper presents a novel Bayesian based controller for snake robots in cluttered environment. It extends the conventional shape based compliant control into statistical field providing an explicit mathematical formulation with Bayesian network. A sequential density propagation rule is derived by introducing several probability densities in a unified framework. Specifically, two input influence densities are proposed to model the cumulative effect of various external forces that the snake robot undergoes. Moreover, the measurement likelihood model is exploited to give a more robust closed loop feedback. Overall, the proposed approach provides an innovative way to handle challenging tasks of snake robot control in complicated environment. Experimental results have been demonstrated for both simulation and real world data.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)