Dynamic locomotion for passive-ankle biped robots and humanoids using whole-body locomotion control

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.

The near-surface velocity reversal and its detection via unsupervised machine learning

In land seismic data processing, picking the first arrivals and imaging the near-surface velocity structures are important tasks. However, in many areas, the near-surface weathering layer includes high-velocity reversals, causing the first arrivals to exhibit shingling effects, which are difficult for picking at the far offset. We have used an acoustic full-waveform modeling method in a multilayered half-space to simulate first arrivals with the velocity reversal. Numerical tests indicate that under certain conditions, shingling occurs if the seismic wave propagates through a thin velocity reversal layer embedded in the shallow structures. Detection of shingling is essential for the selection of valid near-surface imaging solutions, such as first-arrival refraction, or waveform solutions for the appropriate areas. We find that an automated detection scheme that uses unsupervised machine learning can help identify the velocity reversal. We test the method on synthetic and real data, and the testing shows that the automated detection result matches our visual judgment well. After the automated detection, appropriate inversion approaches can be applied to corresponding areas.

Analysis of friction error in CNC machine tools based on electromechanical characteristics

Friction is a kind of inherent and nonlinear disturbance in feed systems, which inevitably deteriorates motion accuracy at velocity reversal. Position error caused by friction is integrally effected by three aspects of feed drives, including command, control, and mechanical subsystems. Unfortunately, the traditional analyses hardly consider all mentioned aspects. Especially, no research has been reported on control characteristic at reverse motion. The purpose of this paper is to reveal the generation mechanism of friction error of a feed drive based on the commercial computer numerical control with three-loop control structure and velocity feedforward and proportional–proportional–integral controllers. Firstly, the generation process of the friction error at velocity reversal is profoundly investigated. Based on it, a simplified control model is conducted to explain transition from presliding to sliding regimes. It is the bond of analyzing friction error from command, control, and mechanical subsystems. Subsequently, the processes of presliding, acceleration, and adjustment stages are analyzed. Moreover, analytical formulas are derived to predict the durations of three stages and describe the shape of friction error. Then, the contour errors of linear and circular motion caused by friction can be predicted online. Experiments are introduced to verify the effectiveness of the proposed methods and formulations.