Abstract
To maintain human-like active balance in a humanoid robot, this article proposes a dynamic priority-based multitask algorithm to avoid self-collision during highly complex robotic whole-body motions and rebalance after external disturbance using momentum compensation strategies. On the one hand, the conflict between self-collision and self-balance constraints in task-space merging with end-effector tracking tasks are considered in the multitask algorithm to improve the robot’s balance. On the other hand, for self-balance during the robot’s movement, momentum compensation is considered as one task and utilized to reject unknown disturbances. Two strategies are put forward to restrain the sudden change in momentum. One is to calculate the correction in the joint space with the resolved momentum control (RMC) method; the other is to add an end-effector tracking motion in the task space. Simulations and experiments on a full-body humanoid robot validate the effectiveness of the proposed method. Experiments on both a simulation and a full-body humanoid are designed to validate the task-prior algorithm. With the proposed method, the humanoid robot succeeds in avoiding self-collision during movement and is able to rectify itself to the preplanned steady stance while encountering undefined external forces.
Highlights
Robust locomotion is fundamental for a humanoid robot to maneuver in an unstructured and undetermined human environment
Humanoid walking is commonly realized by planning the center of mass (CoM) trajectories so that the resultant zero moment point (ZMP)[11,12] trajectory follows a desired ZMP trajectory
Most of the current walking control methods adopt predefined foot and center of mass trajectories and control the robot with simplified dynamics model. These methods usually cannot resist with external disturbance and lack adaptation to the environment changes and can be hardly used in real on-field applications
Summary
Robust locomotion is fundamental for a humanoid robot to maneuver in an unstructured and undetermined human environment. During the robot support stage, the control of the robot’s foot is only concerned with its position and position tracking during the rising and falling phases and does not care about the movement of the foot in the swing phase At this time, the swing foot can be used to complete the more important task of momentum compensation. A more reasonable strategy in robot motion control is that the control subtasks can dynamically switch priorities according to the current control needs during the movement of the robot and realize more flexible multitask cooperative control. The resolved motion rate control (RMRC) method/differential inverse kinematics (DIK) is introduced to solve the redundant problem.[15] The null-space method is utilized to address task priority.[16] The influences of external perturbations on robots can be regarded as the offset of CoM and the change in momentum. We conclude this study and suggest future work in the seventh section
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