Active patient participation in the rehabilitation process after stroke has been shown to accelerate neural remodeling. The control framework of rehabilitation robots should provide appropriate assistive forces to users. An assist-as-needed (AAN) control method is proposed to help users to move upper limbs in the workspace freely, and to control the exoskeleton to provide assistance. The method is based on zero moment control (ZMC), helping the user achieve robotic traction with minimal interaction force. Based on the posture of the upper arm and forearm, an AAN controller can modify assistive forces at two human–robot-interaction (HRI) points along the direction opposite to gravity. A shoulder motion prediction model is proposed to enable the exoskeleton to mimic the user’s upper limb natural movements. In order to improve the transparency during rehabilitation training, a nonlinear numerical friction model based on the Stribeck friction model is developed. A healthy adult male was recruited to perform various activities of daily living (ADL) tests to assess the effectiveness of the controllers. The experimental results show that the proposed ZMC controller has high HRI transparency and can control the exoskeleton to complete a wide range of upper limb movements, and the maximum interaction force and torque can be captured within −7.76 N and 4.58 Nm, respectively. The AAN controller can provide appropriate assistance in the desired direction, and the exoskeleton maintains kinematic synchronization with the user’s shoulder during shoulder girdle movement.

In deep underground engineering applications, such as coal mining, coal–rock masses are frequently subjected to repeated loading and unloading conditions. Understanding the evolution mechanisms of their internal three-dimensional fracture fields has become a critical scientific challenge. This study utilized X-ray Microscopy (XRM) to observe changes in internal fractures of coal samples after each loading–unloading cycle, reconstructing the internal fractures and mineral particles. Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) were employed to analyze the surface morphology and mineral composition of coal sample cross-sections. The experimental results revealed that: (1) With an increasing number of loading–unloading cycles, the samples’ volumes initially decreased and then expanded, with the expansion accompanied by rapid propagation of CT-scale fractures; (2) During the linear elastic phase, micro-fractures developed progressively but remained small, while sustained stress caused these fractures to interconnect, eventually leading to macroscopic failure; (3) Hard mineral particles within the coal samples, such as iron ore, acted as barriers to crack propagation. These findings indicate that the evolution characteristics of the internal fracture fields in coal–rock masses are influenced by stress state, pre-existing fractures, and the distribution of mineral particles.

The limitations of traditional fossil fuels have prompted researchers to develop new renewable energy technologies. Raindrop impact energy has become a research hotspot in the field of energy harvesting due to its wide distribution and renewability, especially in the self-energy supply of microrobots. The energy harvester is installed on the robot, utilizing piezoelectric-energy-harvesting technology to achieve self-energy supply for the robot, but the efficiency of existing raindrop energy harvesters is unsatisfactory. In order to better collect the impact energy of raindrops and broaden the application of piezoelectric energy harvesters in the field of autonomous energy supply of robots, inspired by the vibration generated by raindrop excitation of plant leaves in nature, a raindrop energy harvester for autonomous energy supply for robots was proposed through the bionic leaf design, a mathematical model was established for numerical simulation analysis, and the effects of excitation position, excitation height, petiole length and excitation rate on the output performance of the harvester were analyzed. Numerical simulation and experimental test results show that the piezoelectric energy harvester has a higher output at the excitation position at the tip. The higher the excitation height of the water droplet, the higher the output voltage. Increasing the length of the petiole can significantly improve its performance output, and at the same time, the raindrop excitation rate will also affect its output to a certain extent.

Post-disaster underground mine rescue requires environmental perception and remote operation before personnel can enter hazardous areas. In this article, we describe the system-level integration of a tracked detection robot, a 1432–1442 MHz wireless Mesh link, three deployable relay beacons, a handheld terminal, and multi-parameter sensing. The radio modules use 0.5 W transmit power, a nominal 30 Mbps data rate, and vertically polarized 5 dBi omnidirectional antennas, while the analytical formulation links differential-drive commands, path-loss and link-quality estimates, and a consecutive-sample beacon deployment rule. Physical records from three mine rescue test facilities document manual motion, navigation interface operation, Mesh topology formation, environmental data display, and audio/video return. While these records establish prototype feasibility and functional-chain continuity, they do not provide retained quantitative RSSI, packet loss, latency, trajectory error, stopping distance, endurance, or repeated-trial statistics. Accordingly, the proposed models are presented as design and measurement frameworks rather than validated predictors or evidence of performance superiority.