Soft robotic hands provide better safety and adaptability than rigid robotic hands. Furthermore, a multijointed structure that imitates the movement of a human hand represents significant progress in realizing its anthropomorphism. In this study, we present a multijointed pneumatic soft anthropomorphic hand that is capable of expressing letters through sign language and grasping different objects using three grasping modes, namely thumb grasping, precision grasping, and power grasping. This novel soft hand is composed of multijointed soft fingers, a thumb, thenar, and 3D-printed palm. Tests were performed to characterize the displacement track and force performance of the fingers, thumb, and thenar, which was made by mold casting silicone rubber. In addition, a dedicated pneumatic control system was designed and built to enable the soft hand to automatically perform the tasks set by specific programs. This new multijointed hand with a flexible thenar represents significant progress in the development of anthropomorphic bionic hands, offering the benefits of fast response, low cost, as well as ease of fabrication, assembly, and replacement.

With the popularization of electric vehicles, early built parking lots cannot solve the charging problem of a large number of electric vehicles. Mobile charging robots have autonomous navigation and complete charging functions, which make up for this deficiency. However, there are static obstacles in the parking lot that are random and constantly changing their position, which requires a stable and fast iterative path planning method. The gray wolf optimization (GWO) algorithm is one of the optimization algorithms, which has the advantages of fast iteration speed and stability, but it has the drawback of easily falling into local optimization problems. This article first addresses this issue by improving the fitness function and position update of the GWO algorithm and then optimizing the convergence factor. Subsequently, the fitness function of the improved gray wolf optimization (IGWO) algorithm was further improved based on the minimum cost equation of the A* algorithm. The key coefficients AC₁ and AC₂ of two different fitness functions, Fitness₁ and Fitness₂, were discussed. The improved gray wolf optimization algorithm integrating A* algorithm (A*-IGWO) has improved the number of iterations and path length compared to the GWO algorithm in parking lots path planning problems.

Humans possess dexterous hands that surpass those of other animals, enabling them to perform intricate, complex movements. Soft hands, known for their inherent flexibility, aim to replicate the functionality of human hands. This article provides an overview of the development processes and key directions in soft hand evolution. Starting from basic multi-finger grippers, these hands have made significant advancements in the field of robotics. By mimicking the shape, structure, and functionality of human hands, soft hands can partially replicate human-like movements, offering adaptability and operability during grasping tasks. In addition to mimicking human hand structure, advancements in flexible sensor technology enable soft hands to exhibit touch and perceptual capabilities similar to humans, enhancing their performance in complex tasks. Furthermore, integrating machine learning techniques has significantly promoted the advancement of soft hands, making it possible for them to intelligently adapt to a variety of environments and tasks. It is anticipated that these soft hands, designed to mimic human dexterity, will become a focal point in robotic hand development. They hold significant application potential for industrial flexible gripping solutions, medical rehabilitation, household services, and other domains, offering broad market prospects.