Squat motion of a bipedal robot using real-time kinematic prediction and whole-body control

Abstract

Squatting is a basic movement of bipedal robots, which is essential in robotic actions like jumping or picking up objects. Due to the intrinsic complex dynamics of bipedal robots, perfect squatting motion requires high-performance motion planning and control algorithms. The standard academic solution combines model predictive control (MPC) with whole-body control (WBC), which is usually computationally expensive and difficult to implement on practical robots with limited computing resources. The real-time kinematic prediction (RKP) method is proposed, which considers upcoming reference motion trajectories and combines it with quadratic programming (QP)-based WBC. Since the WBC handles the full robot dynamics and various constraints, the RKP only needs to adopt the linear kinematics in the robot's task space and to softly constrain the desired accelerations. Then, the computational cost of derived closed-form RKP is greatly reduced. The RKP method is verified in simulation on a heavy-loaded bipedal robot. The robot makes rapid and large-amplitude squatting motions, which require close-to-limit torque outputs. Compared with the conventional QP-based WBC method, the proposed method exhibits high adaptability to rough planning, which implies much less user interference in the robot's motion planning. Furthermore, like the MPC, the proposed method can prepare for upcoming motions in advance but requires much less computation time.