Shugen Ma

Min Yang, Yang Tian, Longchuang Li et al.

In-pipe inspection robots must traverse confined pipeline networks with elbows and three-dimensional fittings, requiring both reliable axial traction and rapid rolling reorientation for posture correction. In compact V-shaped platforms, these functions often rely on shared contacts or indirect actuation, which introduces strong kinematic coupling and makes performance sensitive to geometry and friction variations. This paper presents a V-shaped in-pipe robot with a joint-axis-and-wheel-separation layout that provides two physically independent actuation channels, with all-wheel-drive propulsion and motorized rolling reorientation while using only two motors. To make the decoupling mechanism explicit and designable, we formulate an actuation transmission matrix and identify the spherical-wheel contact angle as the key geometric variable governing the dominant roll-to-propulsion leakage and roll-channel efficiency. A geometric transmission analysis maps mounting parameters to the contact angle, leakage, and efficiency, yielding a structural guideline for suppressing crosstalk by driving the contact angle toward zero. A static stability model further provides a stability-domain map for selecting torsion-spring stiffness under friction uncertainty to ensure vertical-pipe stability with a margin. Experiments validate the decoupling effect, where during high-dynamic rolling in a vertical pipe, the propulsion torque remains nearly invariant. On a multi-material testbed including out-of-plane double elbows, the robot achieved a 100% success rate in more than 10 independent round-trip trials.

Lijiang Liu, Junyu Shi, Yong Sun et al.

Current exoskeleton control methods often face challenges in delivering personalized treatment. Standardized walking gaits can lead to patient discomfort or even injury. Therefore, personalized gait is essential for the effectiveness of exoskeleton robots, as it directly impacts their adaptability, comfort, and rehabilitation outcomes for individual users. To enable personalized treatment in exoskeleton-assisted therapy and related applications, accurate recognition of personal gait is crucial for implementing tailored gait control. The key challenge in gait recognition lies in effectively capturing individual differences in subtle gait features caused by joint synergy, such as step frequency and step length. To tackle this issue, we propose a novel approach, which uses Multi-Scale Global Dense Graph Convolutional Networks (GCN) in the spatial domain to identify latent joint synergy patterns. Moreover, we propose a Gait Non-linear Periodic Dynamics Learning module to effectively capture the periodic characteristics of gait in the temporal domain. To support our individual gait recognition task, we have constructed a comprehensive gait dataset that ensures both completeness and reliability. Our experimental results demonstrate that our method achieves an impressive accuracy of 94.34% on this dataset, surpassing the current state-of-the-art (SOTA) by 3.77%. This advancement underscores the potential of our approach to enhance personalized gait control in exoskeleton-assisted therapy.

Tianhao Chen, Limei Cheng, Yang Liu et al.

Continuous reinforcement learning such as DDPG and A3C are widely used in robot control and autonomous driving. However, both methods have theoretical weaknesses. While DDPG cannot control noises in the control process, A3C does not satisfy the continuity conditions under the Gaussian policy. To address these concerns, we propose a new continues reinforcement learning method based on stochastic differential equations and we call it Incremental Reinforcement Learning (IRL). This method not only guarantees the continuity of actions within any time interval, but controls the variance of actions in the training process. In addition, our method does not assume Markov control in agents' action control and allows agents to predict scene changes for action selection. With our method, agents no longer passively adapt to the environment. Instead, they positively interact with the environment for maximum rewards.