A Learning-Free Method for Locomotion Mode Prediction by Terrain Reconstruction and Visual-Inertial Odometry.

This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human movements and sparse features. Evidence of its effectiveness, both in terms of accuracy and operational time consumption, is substantiated through tests conducted using open-source dataset and closed-loop evaluations. Comprehensive experiments were undertaken to validate its prediction accuracy across various test conditions such as subjects, scenarios, sensor mounting positions, camera views, step sizes, walking directions, and disturbances from moving obstacles. A comprehensive prediction accuracy rate of 99.00% confirms the efficacy, generality, and robustness of the proposed method.

Terrain-Perception-Free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation.

Dynamic quadrupedal locomotion over rough terrains reveals remarkable progress over the last few decades. Small-scale quadruped robots are adequately flexible and adaptable to traverse uneven terrains along the sagittal direction, such as slopes and stairs. To accomplish autonomous locomotion navigation in complex environments, spinning is a fundamental yet indispensable functionality for legged robots. However, spinning behaviors of quadruped robots on uneven terrain often exhibit position drifts. Motivated by this problem, this study presents an algorithmic method to enable accurate spinning motions over uneven terrain and constrain the spinning radius of the center of mass (CoM) to be bounded within a small range to minimize the drift risks. A modified spherical foot kinematics representation is proposed to improve the foot kinematic model and rolling dynamics of the quadruped during locomotion. A CoM planner is proposed to generate a stable spinning motion based on projected stability margins. Accurate motion tracking is accomplished with linear quadratic regulator (LQR) to bind the position drift during the spinning movement. Experiments are conducted on a small-scale quadruped robot and the effectiveness of the proposed method is verified on versatile terrains including flat ground, stairs, and slopes.

Somatosensory actuator based on stretchable conductive photothermally responsive hydrogel.

Mimicking biological neuromuscular systems' sensory motion requires the unification of sensing and actuation in a singular artificial muscle material, which must not only actuate but also sense their own motions. These functionalities would be of great value for soft robotics that seek to achieve multifunctionality and local sensing capabilities approaching natural organisms. Here, we report a soft somatosensitive actuating material using an electrically conductive and photothermally responsive hydrogel, which combines the functions of piezoresistive strain/pressure sensing and photo/thermal actuation into a single material. Synthesized through an unconventional ice-templated ultraviolet-cryo-polymerization technique, the homogenous tough conductive hydrogel exhibited a densified conducting network and highly porous microstructure, achieving a unique combination of ultrahigh conductivity (36.8 milisiemens per centimeter, 103-fold enhancement) and mechanical robustness, featuring high stretchability (170%), large volume shrinkage (49%), and 30-fold faster response than conventional hydrogels. With the unique compositional homogeneity of the monolithic material, our hydrogels overcame a limitation of conventional physically integrated sensory actuator systems with interface constraints and predefined functions. The two-in-one functional hydrogel demonstrated both exteroception to perceive the environment and proprioception to kinesthetically sense its deformations in real time, while actuating with near-infinite degrees of freedom. We have demonstrated a variety of light-driven locomotion including contraction, bending, shape recognition, object grasping, and transporting with simultaneous self-monitoring. When connected to a control circuit, the muscle-like material achieved closed-loop feedback controlled, reversible step motion. This material design can also be applied to liquid crystal elastomers.