Soft robots are characterized by ultraflexible electromechanical structures that are capable of large deformations and movements. However, the challenge of generating the high output forces necessary for manipulation persists because of the low Young’s moduli of soft materials, which limit their effectiveness in tasks such as carrying, grasping, and providing support. Inspired by shrimp exoskeletons, we designed an origami exoskeleton that features flexible-rigid arrangements and mechanical multistability. This design offers high stiffness in stable states while allowing for long-stroke transitions, making it suitable for integration with soft actuators to balance deformability and load-bearing capacity. With advantages such as high compressibility and rapid integration, these exoskeleton-enhanced arms have promising applications when mounted on unmanned ground and aerial vehicles, facilitating large-scale dexterous manipulation with the necessary load capacities. For existing soft arms, the proposed origami exoskeleton enhances the potential for manipulation within their original design framework.

This paper investigates the fluidic leak rate through metal sealing surfaces by developing fractal models for the contact process and leakage process. An improved model is established to describe the seal-contact interface of two metal rough surface. The contact model divides the deformed regions by classifying the asperities of different characteristic lengths into the elastic, elastic-plastic and plastic regimes. Using the improved contact model, the leakage channel under the contact surface is mathematically modeled based on the fractal theory. The leakage model obtains the leak rate using the fluid transport theory in porous media, considering that the pores-forming percolation channels can be treated as a combination of filled tortuous capillaries. The effects of fractal structure, surface material and gasket size on the contact process and leakage process are analyzed through numerical simulations for sealed ring gaskets.

With the rapid advancement of UAV technology, the utilization of multi-UAV cooperative operations has become increasingly prevalent in various domains, including military and civilian applications. However, achieving efficient coordinated rounding-up of multiple UAVs remains a challenging problem. This paper addresses the issue of collaborative drone hunting by proposing a decision-making control model based on deep reinforcement learning. Additionally, a shared experience data pool is established to facilitate communication between drones. Each drone possesses independent decision-making and control capabilities while also considering the presence of other drones in the environment to collaboratively accomplish obstacle avoidance and rounding-up tasks. Furthermore, we redefine and design the reward function of reinforcement learning to achieve precise control of drone swarms in diverse environments. Simulation experiments demonstrate the feasibility of the proposed method, showcasing its successful completion of obstacle avoidance, tracking, and rounding-up tasks in an obstacle environment.