In the field of wheel-legged robots (WLRs), two types of configurations exist: toe-wheeled and knee-wheeled. In the knee-wheeled configuration, the robot is capable of switching between leg walking and wheel driving modes. However, the participation of the calf leg in driving control is often limited during the switch to the wheel driving mode, which makes it similar to a rocker-arm suspension vehicle with an additional unsprung mass. The presence of redundant calf legs in knee-wheeled WLRs can increase the unsprung mass, potentially impacting the overall performance of the robot. To address this issue, this paper presents a collaborative control method based on road preview for knee-wheeled WLRs. The proposed approach simplifies the wheeled system to a quarter car model and designs a Model Predictive Controller (MPC). By effectively integrating the redundant calf legs into the control strategy, the proposed approach aims to mitigate the negative effects of increased unsprung mass and enhance the overall performance of the robot in terms of vertical comfort and road holding ability. Simulation results demonstrate that the vertical performance of the wheel leg system can be effectively improved through the control of the calf leg when traversing undulating terrains. The proposed anticipatory control method and collaborative controller were found to effectively enhance the overall performance of the robot in terms of comfort and road holding ability. The findings of this study provide insights into the development of control strategies for knee-wheeled legged robots and contribute to the advancement of wheeled legged locomotion research.


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    Title :

    Wheel-Leg Collaborative Control for Wheel-legged Robots Based on MPC with Preview


    Contributors:
    Pan, Zheng (author) / Li, Boyuan (author) / Jing, Hui (author) / Niu, Zhihua (author) / Wang, Rongrong (author)


    Publication date :

    2023-10-16


    Size :

    2159006 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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