4.6 Article

Virtual Model Control for Quadruped Robots

期刊

IEEE ACCESS
卷 8, 期 -, 页码 140736-140751

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3013434

关键词

Legged locomotion; Robot kinematics; Dynamics; Control systems; Solid modeling; Computational modeling; Virtual model control; dynamic balance control; anti-disturbance control; trajectory tracking control; quadruped robots

资金

  1. Fundamental Research Funds for the Central Universities [2019JBM051]
  2. Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention Open Foundation for Research [RRC201701]
  3. Beijing Natural Science Foundation [3204051]
  4. Science and Technology Research Project of China Railway Corporation [P2018G047]

向作者/读者索取更多资源

Virtual model control is a motion control framework that uses virtual components to create virtual forces/torques, which are actually generated by joint actuators when the virtual components interact with robot systems. Firstly, this paper employs virtual model control to do a dynamic balance control of whole body of quadruped robots' trot gait in a bottom controller. In each leg, there exists a designed swing phase virtual model control and a stance phase counterparts. In the whole body, virtual model control is utilized to achieve a attitude control containing roll, pitch and yaw. In the attitude control, a forces/torques distribution method between two stance legs is pre-investigated. In a high-level implemented controller, an intuitive velocity control approach proposed by Raibert is applied for the locomotion of quadruped robots. Secondly, an anti-disturbance control, which contains compensating gravity, adjusting step length, adjusting swing trajectory, adjusting attitude, and adjusting virtual forces/torques, is investigated to improve the robustness, terrain adaptability, and dynamic balance performance of quadrupedal locomotion. Thirdly, a trajectory tracking control method based on an intuitive velocity control is addressed through considering four factors: terrain complexity index, curvature radius of given trajectory, distance to terminal, and maximum velocity of quadruped robots. Finally, simulations validate the effectiveness of proposed controllers.

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