Whole-body control (WBC) is a generic task-oriented control method for feedback control of loco-manipulation behaviors in humanoid robots. The combination of WBC and model-based walking controllers has been widely utilized in various humanoid robots. However, to date, the WBC method has not been employed for unsupported passive-ankle dynamic locomotion. As such, in this article, we devise a new WBC, dubbed the whole-body locomotion controller (WBLC), that can achieve experimental dynamic walking on unsupported passive-ankle biped robots. A key aspect of WBLC is the relaxation of contact constraints such that the control commands produce reduced jerk when switching foot contacts. To achieve robust dynamic locomotion, we conduct an in-depth analysis of uncertainty for our dynamic walking algorithm called the time-to-velocity-reversal (TVR) planner. The uncertainty study is fundamental as it allows us to improve the control algorithms and mechanical structure of our robot to fulfill the tolerated uncertainty. In addition, we conduct extensive experimentation for: (1) unsupported dynamic balancing (i.e., in-place stepping) with a six-degree-of-freedom biped, Mercury; (2) unsupported directional walking with Mercury; (3) walking over an irregular and slippery terrain with Mercury; and 4) in-place walking with our newly designed ten-DoF viscoelastic liquid-cooled biped, DRACO. Overall, the main contributions of this work are on: (a) achieving various modalities of unsupported dynamic locomotion of passive-ankle bipeds using a WBLC controller and a TVR planner; (b) conducting an uncertainty analysis to improve the mechanical structure and the controllers of Mercury; and (c) devising a whole-body control strategy that reduces movement jerk during walking.

Visual information is indispensable to human locomotion in complex environments. Although amputees can perceive the environmental information by eyes, they cannot transmit the neural signals to prostheses directly. To augment human-prosthesis interaction, this article introduces a subvision system that can perceive environments actively, assist to control the powered prosthesis predictively, and accordingly reconstruct a complete vision-locomotion loop for transfemoral amputees. By using deep learning, the subvision system can classify common static terrains (e.g., level ground, stairs, and ramps) and estimate corresponding motion intents of amputees with high accuracy (98%). After applying the subvision system to the locomotion control system, the powered prosthesis can help amputees to achieve nonrhythmic locomotion naturally, including switching between different locomotion modes and crossing the obstacle. The subvision system can also recognize dynamic objects, such as an unexpected obstacle approaching the amputee, and assist in generating an agile obstacle-avoidance reflex movement. The experimental results demonstrate that the subvision system can cooperate with the powered prosthesis to reconstruct a complete vision-locomotion loop, which enhances the environmental adaptability of the amputees.

Overhead manipulation tasks often require collaborations between two operators, which becomes challenging in confined spaces such as in a compartment. Supernumerary Robotic Limb (SuperLimb), as a promising wearable robotic solution, can provide assistance in terms of broader workspace, wider manipulation functionalities and safer working conditions. However, the safety concerns of human-centered SuperLimb interaction mechanisms are rarely studied to date, particularly regarding human standing balance. This study proposes a balance controller by which one individual operator can accomplish overhead tasks with the assistance of SuperLimb via tunable interaction force and supporting force regulation. The SuperLimb-human interaction is modeled and a dynamics control method based on QR decomposition (also known as QR factorization, in which a matrix is factorized into an orthogonal matrix and an upper triangular matrix) is adopted to decouple joint torques of the SuperLimb and the interaction forces. Therefore, the supporting forces can be regulated independently to guarantee the operator-SuperLimb interaction forces in a safe region. Force plate is used for measuring the CoP position as an evaluation method of the standing balance. The critical horizontal push force is learned through experiment to guide the balance controller. This method is implemented on a SuperLimb prototype worn on the operator's back, to provide necessary supporting forces on overhead object while allowing the operator to move freely underneath.

Summary: To improve biped locomotion’s robustness to internal and external disturbances, this study proposes a hierarchical structure with three control levels. At the high level, a foothold sequence is generated so that the Center of Mass (CoM) trajectory tracks a planned path. The planning procedure is simplified by selecting the midpoint between two consecutive Center of Pressure (CoP) points as the feature point. At the middle level, a novel robust hybrid controller is devised to drive perturbed system states back to the nominal trajectory within finite cycles without chattering. The novelty lies in that the hybrid controller is not subject to linear CoM dynamic constraints. The hybrid controller consists of two sub-controllers: an oscillation controller and a smoothing controller. For the oscillation controller, the desired CoM height is specified as a sine-shaped function, avoiding a new attractive limit cycle. However, this controller results in the inevitable chattering because of discontinuities. A smoothing controller provides continuous properties and thus can inhibit the chattering problem, but has a smaller region of attraction compared with the oscillation controller. A hybrid controller merges the two controllers for a smooth transition. At the low level, the desired CoM motion is defined as tasks and embedded in a whole body operational space (WBOS) controller to compute the joint torques analytically. The novelty of the low-level controller lies in that within the WBOS framework, CoM motion is not subject to fixed CoM dynamics and thus can be generalized.

Estimation of center of mass (CoM) and center of pressure (CoP) is critical for lower limb exoskeletons, prostheses, and legged robots. To meet the demand in these fields, this study presents a novel CoM and CoP estimation method for human walking through a wearable visual odometry (VO) device. This method is named VO-based estimation of CoM and CoP (VOECC). The methodology of VOECC is that the VO provides CoM trajectory estimation and the inherent walking dynamics model is exploited as prior knowledge for CoP trajectory estimation during human walking. Gait cycle is estimated based on the frequency analysis of the CoM trajectory, which is cropped into segments. Each segment mainly includes a half gait cycle. The segments are designed to be sliding to mitigate the disturbance of double-stance phase. For each segment, a quadratic programming (QP) problem is formulated to fit the CoM measurement with the theoretical walking dynamics model. The solution to this QP problem is an optimal gait parameters estimation, including CoP. Based on this solution, the human walking model with the CoM trajectory and CoP excursion is reconstructed. VOECC is evaluated experimentally where human walks on level ground and upstairs with VO device attached in front of the chest. The ground truth of CoM and CoP position is directly measured by the motion capture system and fully instrumented treadmill, respectively, and compared with the VOECC results. The proposed method is demonstrated to be effective in terms of wearable and extensible functionalities compared with the existing methods. Root-mean-squared errors between the CoP measured by fully instrumented treadmill and the CoP estimated by VOECC are evaluated and compared. This method has the potential to be extensible in lower limb rehabilitation, prosthetic, and legged locomotion fields.

Quadruped robots manifest great potential to traverse rough terrains with payload. Current model-based controllers, which are extensively adopted in quadruped robot locomotion control, rely on accurate estimation of parameters and will significantly deteriorate in severe disturbance, e.g., adding heavy payload. This article introduces an online identification method, which is named as adaptive control for quadruped locomotion, to address model uncertainties. Meanwhile, a concurrent adaptive controller is also indispensable to accomplish identification and locomotion. Therefore, this article presents an adaptive control algorithm based on the online payload identification for the high payload capacity (the ratio between payload and robot’s self-weight) quadruped locomotion. The newly proposed algorithm could achieve estimating and compensating the external disturbances induced by the payload online. The tracking accuracy of the robot’s center of mass and orientation trajectories for the identification task is highly improved. The locomotion task can be incorporated in inverse-dynamics-based quadratic programming, realizing a trotting gait. The proposed method is verified in a real quadruped robot platform. Experiments prove the estimation efficacy for the payload weighing from 20 to 75 $\rm kg$ and loaded at different locations of the robot’s torso.