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Toward swift running on biped robots, this article provides a complete design approach for a robot leg featuring reduced rotational inertia and introduces a novel mechanic optimization scheme to meet severe mechanical requirements. We adopt a ballscrew driving setup to design the inertia-reduced driving for the leg. When designing the actuator's transmission to satisfy the joints' speed and torque demands, it is challenging to specify the mechanism parameters because their effects are nonlinear and highly coupled. To address this problem, we invite a novel idea that uses target motion trajectories to guide the optimization process and make the mechanism fit the motion best. For that purpose, the joint space position is divided into a high-speed and high-torque region by analyzing the target joint trajectories. Then, the optimization scheme is formulated according to this division to obtain the optimized joint position–transmission ratio curve. The proposed technique can dramatically reduce the torque demands, making a good tradeoff in reconciling the dilemma between the joint speed and torque demands. Finally, the proposed scheme is validated with a 7 km/h running motion on the biped robot BHR-T, enhancing the robot with the desired athletic capacities.

To generate dynamically feasible running motion for humanoid robots, one conventionally chooses between a simplified model and a full-body model. A simplified model can be calculated at high rates but ignores many of the robot's dynamic features, while a full-body model usually leads to time-consuming nonlinear programming. This article explores a powerful middle ground that takes the advantages of both worlds and proposes a running pattern generation method that can optimize the whole-body motion online. A three-body model generates the centroidal motion, including the center of mass position and the angular momentum. Then, we propose the so-called reference-tracking dynamics, where the collocated motion references in task space are projected to the joint space with serial quadratic programming. The separation of collocation and projection greatly reduces the scale of the problem. In addition, the two major motion references, centroidal and foot motion, can be handled separately to furtherly simplify the problem. Experiments on the humanoid robot platform BIT Humanoid Robot - Test (BHR-T) show that this method can generate fast and dynamically feasible running motion online, where running at 6 km/h is achieved.

Wheeled-legged humanoid robots combine the rough terrain compliance of humanoid robots with the high efficiency of wheeled robots, enabling the robot to achieve flexible and stable locomotion over multiple terrains. However, the stability control of the wheel-legged humanoid robot in dealing with rough terrains and unexpected external disturbances remains unsolved. In the current investigation, a compliant balance control framework (CBCF) is proposed, which can absorb ground shocks, withstand unexpected external disturbances, and remain stable posture during motion. The CBCF connects the control of legs movement and the wheels balance control through the movement of the robot's center of mass. The wheel balance control employs the inverted pendulum model and controls the two wheels through model prediction. The leg posture control utilizes a whole-body dynamic compensator to realize the compliant motion and remain a stable posture. Cooperating with the high-level motion planner, the CBCF can allow the BHR-WI to move quickly and perform excellent adaptation in unmodeled rough terrains, and it is able to appropriately handle unexpected external disturbances as well. It is also worth mentioning that the BHR-WI is capable of remaining balance and quickly recovering stability in the event of a disturbance, even if one of the legs leaves the ground. Finally, tests confirm that the BHR-WI could withstand sustained unexpected disturbances, could smoothly cross the grass and steps and even realize high maneuverability in jumping.

In previous research on robot driving, most steering wheel turning skills are implemented with a single arm. This article introduces a model-based method for bimanual steering wheel turning, enabling a “seated” dual-arm robot to perform this manipulation without precise knowledge of the steering wheel's location and orientation. In this method, the dual-arm robot and the steering wheel are modeled as a system by building contact constraints in a quadratic program (QP) controller. Then, the dual-arm coordinated motion can be induced automatically rather than planned explicitly. In addition, the proposed method includes an adaptation scheme. The scheme can estimate and reduce the discrepancies between reality and the model according to the executed end-effector trajectories. With its help, the dual-arm robot effectively minimizes internal forces and achieves fast turning. The proposed method is validated through experiments on a real vehicle and a real dual-arm robotic platform.

In this article, a new 7-DoF humanoid arm that can operate a steering wheel anthropomorphically, and a task-based design method for such redundant manipulators that work with loads in constrained spaces are devised. The latter consists of a controller-integrated optimization method (COM) for structural parameter optimization, and a combinatorial filtration method (CFM) for actuator selection. The COM is based on kinetostatic analysis and implemented through the covariance matrix adaptation evolutionary strategy (CMA-CS). In the optimization, a collision-free control (CFC) method is integrated for the accurate workspace evaluation and the manipulability index for characterizing the kinematic performance. The CFM is based on the results of the COM and a dynamic analysis. In actuator selection, a library of available candidate motors and reducers is constructed for global selection based on the design objectives and the characteristics for more accurate joint parameters. The effectiveness of CFC in structural parameter optimization is verified in the CFM. Thus, a 7-DoF humanoid arm for driving is achieved; its performance is evaluated in real-world experiments to assess the validity of the design.