Motion planning and tracking control of a four-wheel independently driven steered mobile robot with multiple maneuvering modes

Abstract

Safe and effective autonomous navigation in dynamic environments is challenging for four-wheel independently driven steered mobile robots (FWIDSMRs) due to the flexible allocation of multiple maneuver modes. To address this problem, this study proposes a novel multiple mode-based navigation system, which can achieve efficient motion planning and accurate tracking control. To reduce the calculation burden and obtain a comprehensive optimized global path, a kinodynamic interior–exterior cell exploration planning method, which leverages the hybrid space of available modes with an incorporated exploration guiding algorithm, is designed. By utilizing the sampled subgoals and the constructed global path, local planning is then performed to avoid unexpected obstacles and potential collisions. With the desired profile curvature and preselected mode, a fuzzy adaptive receding horizon control is proposed such that the online updating of the predictive horizon is realized to enhance the trajectory-following precision. The tracking controller design is achieved using the quadratic programming (QP) technique, and the primal–dual neural network optimization technique is used to solve the QP problem. Experimental results on a real-time FWIDSMR validate that the proposed method shows superior features over some existing methods in terms of efficiency and accuracy.

Key words： mobile robot; multiple maneuvering mode; motion planning; tracking control; receding horizon control

Acknowledgements

The work was funded in part by the Guangdong Major Science and Technology Project, China (Grant Nos. 2019B090919003 and 2017B090913001), in part by the China Postdoctoral Science Foundation (Grant No. 2019M650179), in part by the Guangdong Innovative and Entrepreneurial Research Team Program, China (Grant No. 2019ZT08Z780), in part by the Dongguan Innovative Research Team Program, China (Grant No. 201536000100031), and in part by the Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, China (Grant No. 2020B1212060014).

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