This paper proposes a parallelogram-based adaptive grasping device to overcome the limitations of conventional rigid grippers. It integrates adaptive parallelogram units and a 2-RRR planar parallel driving mechanism. Screw theory and kinematic analysis validate its performance, while a bevel gear set minimizes driving motors. Grasping tests confirm its favorable dimensional adaptability, providing theoretical and engineering references for adaptive grasping equipment design.

Ice and strong winds can seriously damage transmission lines and disrupt power supply. This review compares major methods used to understand and predict these effects, from physical and numerical models to statistical and data-driven approaches. It shows that no single method is sufficient and highlights the value of combining complementary methods to improve risk assessment, resilience, and future decision-making.

Autonomous vehicles must detect nearby road users quickly and reliably, even when objects are partly hidden. We developed a lightweight camera-based method that adapts its processing to scene difficulty, combines information across video frames, and learns from laser-sensor data during training. It reached 38.7% on the benchmark detection measure and processed 26.2 frames per second without laser sensors during use, showing strong potential for fast, lower-cost vehicle perception.

Aiming at stick failures under hard‑soil excavation, this paper presents a mechanism‑structure collaborative optimization for a 20‑ton excavator. With test‑identified hazardous conditions and Kriging surrogate models, NSGA‑II‑based two‑level optimization boosts digging force by 9 %, cuts peak stress by 7 % and reduces stick weight by 9 %. It provides references for excavator‑attachment multidisciplinary optimization.

Robotic grasping in complex environments is challenging due to variations in object positions, orientations, and backgrounds. This study proposes a vision-based method for collaborative robots that integrates object detection, target mask generation, and grasp position estimation to improve grasping reliability. Experiments on a robot platform demonstrate effective target-specific grasping in unstructured multi-object scenes, showing its potential for intelligent manufacturing applications.