Compliance control is important for the realization of disturbance absorption in biped robots. However, under a sustained disturbance, compliance control causes the robot’s balance to deteriorate because of its floating base nature. Humans address this problem by resisting external disturbance. When pushed, a human will reconcile their posture with the applied external force and then push back to maintain balance. Inspired by this behavior, we propose a compliance control strategy for biped robots called resistant compliance, which allows a robot to comply with the external disturbance initially and then repel the disturbance to reduce the imbalance caused by the reconciliatory motion. As a result, the robot can obtain improved environment-interaction stability and react more like a typical human, thus making both its locomotion and its interactions more stable and safer. To realize this control strategy, the virtual-mass-model (VMM) control is redesigned to unify the disturbances from unexpected external forces and an inclined floor. Then, the VMM control is combined with the linear-inverted-pendulum model to realize resistant compliant motion. Model predictive control is used to track the reference zero-moment-point trajectory, which is essential for locomotion. To validate the proposed control strategy, the method is implemented on the human-sized humanoid robot BHR-T.

Stability control for humanoid robots based on zero moment point (ZMP) control and impedance control are widespread. However, uncertain changes in the center of mass (CoM) height for ZMP control and specific regulation of the variable stiffness of impedance control have been challenging issues in previous studies. In this article, these two problems are solved by implementing fuzzy control-based regulations. First, the fuzzy ZMP controller, which regulates the feedback gains online based on the CoM height change and CoM tracking errors, is proposed. Second, we propose a fuzzy regulation law for variable stiffness, which is applied for uncertain contact situations and inspired by the pattern of human muscle stiffness. With these two methods, the ground adaptability for humanoid robots is enhanced. The proposed method is validated with experiments on a real robot platform, BHR-T.

The humanoid robot, one of the ideal unmanned systems, has the potential to perform tasks currently carried out by human workers in industry, manufacturing, service, and disaster response. In such complicated environments with unknown disturbances, compliance is important for a humanoid robot, especially a position-controlled one, to maintain balance while performing a task. This article presents a dynamic torso compliance method for position-controlled humanoid robots to adapt to unknown external disturbances while standing or walking. This method comprises two independent controllers. One controller, which is based on a linear inverted pendulum with a flywheel, is used to realize compliant behavior by controlling the torso postures according to the zero moment point. The other controller, which is based on a double inverted pendulum, is adopted to obtain the dynamic torso movements as well as maintain balance through a quadratic-programming-based controller. With the proposed method, controls for compliance and balance are decoupled, and a position-controlled robot can perform dynamic torso compliance and maintain balance while standing or walking. The performance of the proposed method is validated by simulations and experiments on a position-controlled humanoid robot (BHR-T).

Highly dynamic movements such as jumping are important to improve the agility and environmental adaptation of humanoid robots. This article proposes an online optimization method to realize a vertical jump with centroidal angular momentum (CAM) control and landing impact absorption for a humanoid robot. First, the robot's center of mass (CoM) trajectory is generated by nonlinear optimization. Then, a quasi-sliding mode controller is designed to ensure that the robot tracks the CoM trajectory accurately. To avoid unexpected spinning in the flight phase, a center-of-pressure-guided angular momentum controller is designed to stabilize the CAM. The modifications of CoM and CAM are realized by online optimization of dynamic components and inverse dynamics. Two quadratic programming optimizations are utilized to generate feasible contact force/torque and joint acceleration referring to uplevel CoM and CAM controllers. In addition, a viscoelastic model-based controller is designed to absorb the vibration caused by a large contact impact. A simulation and experiment of a 0.5-m high (foot lifting distance) vertical jump are achieved on a humanoid robot platform in this article (Fig. 1).

Humanoid robots are being designed to perform tasks currently carried out by human workers in industry, manufacturing, service, and disaster assistance. To this end, the humanoid robot should be able to walk stably across many types of terrain. However, when traversing a complex unknown environment, it is difficult to realize accurate terrain perception immediately through large data collected by the sensor system, leading to a difference between planned foot landing positions and actual foot landing positions. As a result, an unexpected contact force/torque may affect the stability of the robot. This paper adopts active contact perception instead of terrain perception and proposes a contact force/torque control method based on the viscoelastic model to address this problem. In addition, we design a body stability controller based on tracking the trajectories of the virtual repellent point (VRP) and the divergent component of motion (DCM) to restrain the disturbance caused by the unexpected contact force/torque. Simulations and experiments on the BHR-6P humanoid robot platform demonstrate the proposed contact force/torque control method for walking on indefinite uneven terrain.

For real-world applications, a biped robot should maintain stable walking when subjected to sudden external disturbances. Typically, unexpected changes to body acceleration indicate that a robot is experiencing an external disturbance. This paper presents a biped walking controller for rapid response to large external disturbances. First, a novel adjustment algorithm for foot placement is proposed. Here, Zero-Moment-Point (ZMP) variations are mapped onto the new foothold based on calculations from changes in body acceleration. Second, a novel impact reduction control for foot landing is presented based on abating body vibrations. To avoid false detection triggers and excessive foothold adjustment, a rapid disturbance detection method is established using the body acceleration derivative. Finally, the effectiveness of the proposed methods is validated under simulations and in experiments with an actual biped robot.