Path planning is one of the key technologies for autonomous flight of Unmanned Aerial Vehicle. Traditional path planning algorithms have some limitations and deficiencies in the complex and dynamic environment. In this paper, we propose a deep reinforcement learning approach for threedimensional path planning by utilizing the local information and relative distance without global information.UAV can obtain the limited environmental information nearby in the actual scenario with limited sensor capabilities. Therefore, path planning can be formulated as a Partially Observable Markov Decision Process. The recurrent neural network with temporal memory is constructed to address the partial observability problem by extracting crucial information from historical state-action sequences. We develop an action selection strategy that combines the current reward value and the state-action value to reduce the meaningless exploration. In addition, we construct two sample memory pools and propose an adaptive experience replay mechanism based on the frequency of failure. The simulation experiment results show that our method has significant improvements over Deep Q-Network and Deep Recurrent Q-Network in terms of stability and learning efficiency. Our approach successfully plans a reasonable three-dimensional path in the large-scale and complex environment, and has the perfect ability to avoid obstacles.in the unknown environment.

In recent years, translational plasma medicine (TPM), as a novel application area of plasmas, has attracted much attention of experts from both academic and clinical fields. State-of-the-art of the lab-scale research and clinical trials of the cold atmospheric plasmas (CAPs) in the stomatology are reviewed in detail from the direct and indirect applications of the CAPs. Based on the discussions concerning the relationship between the plasma stomatology and the plasma medicine, it is indicated that it would be an important reference for promoting the TPM starting from the fundamental and application studies in the field of dentistry, which is also one of the most three promising application fields of plasma medicine.

A helicopter is a highly nonlinear system. Its mathematical model is difficult to establish accurately, especially the complicated flapping dynamics. In addition, the forces and moments exerted on the fuselage are very vulnerable to external disturbances like wind gust when flying in the outdoor environment. This paper proposes a composite control scheme which consists of a nonlinear backstepping controller and an extended state observer (ESO) to handle the above problems. The stability of the closed-loop system can be guaranteed based on Lyapunov theory. The external disturbances and model nonlinearities are treated as a lumped disturbance. Meanwhile, the ESO is employed to compensate the influence by estimating the lumped disturbance in real-time. Numerical simulation results are presented to demonstrate that the algorithm can achieve accurate and agile attitude tracking under the external wind gust disturbances even with model uncertainties. When coming to the flight test, a block dropping device was designed to generate a quantifiable and replicable disturbance, and the experimental results indicate that the algorithm introduced above can reject the external disturbance rapidly and track the given attitude command precisely.

In order to fly safely and autonomously in complex environments, UAVs need to be able to plan their trajectories in real-time. This paper proposes an improved B-spline-based trajectory generation method that can generate safe, smooth, and kinodynamically feasible trajectories in real-time. This paper firstly introduces the principle of error upper bound of the B-spline curve and proposes a new trajectory safety assurance method; then, the loss function of trajectory is constructed based on safety, smoothness, and flight time; finally, a parameter adaptive trajectory optimization method is proposed, so that obtain the safe trajectory. Compared with the existing methods, the proposed method has two important improvements: (1) it solves the problem of overly conservative safety distance estimation at control points, improves the trajectory smoothness, and reduces the required flight time; (2) it proposes a trajectory optimization method with adaptive adjustment of safety distance parameters, which improves the quality and success rate of the planned trajectory. We validate our proposed method in simulation and real-world tasks, and the test results show that the method proposed in this paper can significantly improve the quality of the generated trajectory.