In this paper, we propose a novel distributed method based on attention-based deep reinforcement learning using individual reward shaping, for multiple unmanned aerial vehicles (UAVs) cooperative short-range combat mission. Specifically, a two-level attention distributed policy, composed of observation-level and communication-level attention networks, is designed to enable each UAV to selectively focus on important environmental features and messages, for enhancing the effectiveness of the cooperative policy. Moreover, due to the high complexity and stochasticity of the UAV combat mission, the learning of UAVs is tricky and low efficient. To embed knowledge to accelerate the policy learning, a potential-based individual reward function is constructed by implicitly translating the individual reward into the specific form of dynamic action potentials. In addition, an actor-critic training algorithm based on the centralized training and decentralized execution framework is adopted to train the policy network of UAV maneuver decision. We build a three-dimensional UAV simulation and training platform based on Unity for multi-UAV short-range combat missions. Simulation results demonstrate the effectiveness of the proposed method and the superiority of the attention policy and individual reward shaping.

In this paper, we present a low-damping active knee prosthesis (LDKP) with low noise and high backdrivability. The proposed prosthesis is driven by a motor and then decelerated by a four-stage synchronous belt. High backdrivability given by this structure accelerates the prosthetic response. A control system containing several sensors are embedded in the proposed prosthesis to recognize different modes and provide corresponding strategies. Preliminary experiments were carried out on a transfemoral amputee subject, demonstrating the features of low noise, high backdrivability and ability to reproduce natural walking gaits.

This paper presents a bio-inspired below-knee exoskeleton to assist human walking. Different from the passive orthotic devices, the proposed exoskeleton includes powered compliant ankle and toe joints, which can output sufficient power to help the one with exoskeleton relearn normal walking gaits. We first propose a passivity-based dynamic bipedal model to analyze the effects of segmented foot and compliant joints on energetic efficiency and stability of bipedal walking. Starting from the results of theoretical analysis, we construct a below-knee exoskeleton prototype with ankle and toe joints driven by two series-elastic actuators. Experimental results validate the effectiveness of the proposed exoskeleton.