This paper investigates the path planning problem of an unmanned aerial vehicle (UAV) for completing a raid mission through ultra-low altitude flight in complex environments. The UAV needs to avoid radar detection areas, low-altitude static obstacles, and low-altitude dynamic obstacles during the flight process. Due to the uncertainty of low-altitude dynamic obstacle movement, this can slow down the convergence of existing algorithm models and also reduce the mission success rate of UAVs. In order to solve this problem, this paper designs a state detection method to encode the environmental state of the UAV’s direction of travel and compress the environmental state space. In considering the continuity of the state space and action space, the SD-TD3 algorithm is proposed in combination with the double-delayed deep deterministic policy gradient algorithm (TD3), which can accelerate the training convergence speed and improve the obstacle avoidance capability of the algorithm model. Further, to address the sparse reward problem of traditional reinforcement learning, a heuristic dynamic reward function is designed to give real-time rewards and guide the UAV to complete the task. The simulation results show that the training results of the SD-TD3 algorithm converge faster than the TD3 algorithm, and the actual results of the converged model are better.

Unmanned Aerial Vehicle (UAV) path planning research refers to the UAV automatically planning an optimal path to the destination under the corresponding environment, while avoiding collision with obstacles in this process. In order to solve the problem of 3D path planning of UAV in a dynamic environment, a heuristic dynamic reward function is designed to guide the UAV. We propose the Environment Exploration Twin Delayed Deep Deterministic Policy Gradient (EE-TD3) algorithm, which combines the symmetrical 3D environment exploration coding mechanism on the basis of TD3 algorithm. The EE-TD3 algorithm model can effectively avoid collisions, improve the training efficiency, and achieve faster convergence speed. Finally, the performance of the EE-TD3 algorithm and other deep reinforcement learning algorithms was tested in the simulation environment. The results show that the EE-TD3 algorithm is better than other algorithms in solving the 3D path planning problem of UAV.

Unmanned helicopter (UH) is often utilized for raid missions because it can evade radar detection by flying at ultra-low altitudes. Path planning is the key technology to realizing the autonomous action of UH. On the one hand, the dynamically changing radar coverage area and the mountains in the low airspace environment will seriously affect the flight safety of UH. On the other hand, the huge state space of the three-dimensional (3D) environment will also make traditional algorithms difficult to converge. To address the above problems, a memory-enhanced dueling deep Q-network (ME-dueling DQN) algorithm was proposed. First, a comprehensive reward function was designed, which can guide the algorithm to converge quickly and effectively improve the sparse reward problem. Then, we introduced a dual memory pool structure and proposed a memory-enhanced mechanism, which can reduce invalid exploration, further improve the learning efficiency of the algorithm, and make the algorithm more stable. Finally, the path planning ability of the proposed algorithm in multiple experimental environments was verified. Experiments showed that the proposed algorithm has good environmental adaptability and can help UH to accurately identify dangerous areas and plan a safe and reliable flight path.

Unmanned helicopters (UH) can evade radar detection by flying at ultralow altitudes, so as to conduct raids on targets. Path planning is one of the key technologies to realize UH’s autonomous completion of raid missions. Since the probability of UH being detected by radar varies with height, how to accurately identify the radar coverage area to avoid crossing has become a difficult problem in UH path planning. Aiming at this problem, a heuristic deep Q-network (H-DQN) algorithm is proposed. First, as part of the comprehensive reward function, a heuristic reward function is designed. The function can generate dynamic rewards in real time according to the environmental information, so as to guide the UH to move closer to the target and at the same time promote the convergence of the algorithm. Second, in order to smooth the flight path, a smoothing reward function is proposed. This function can evaluate the pros and cons of UH’s actions, so as to prompt UH to choose a smoother path for flight. Finally, the heuristic reward function, the smooth reward function, the collision penalty, and the completion reward are weighted and summed to obtain the heuristic comprehensive reward function. Simulation experiments show that the H-DQN algorithm can help UH to effectively avoid the radar coverage area and successfully complete the raid mission.

Unmanned helicopters (UH) can avoid radar detection by flying at ultra-low altitudes; thus, they have been widely used in the battlefield. The flight safety of UH is seriously affected by moving obstacles such as flocks of birds in low airspace. Therefore, an algorithm that can plan a safe path to UH is urgently needed. Due to the strong randomness of the movement of bird flocks, the existing path planning algorithms are incompetent for this task. To solve this problem, a state-coded deep Q-network (SC-DQN) algorithm with symmetric properties is proposed, which can effectively avoid randomly moving obstacles and plan a safe path for UH. First, a dynamic reward function is designed to give UH appropriate rewards in real time, so as to improve the sparse reward problem. Then, a state-coding scheme is proposed, which uses binary Boolean expression to encode the environment state to compress environment state space. The encoded state is used as the input to the deep learning network, which is an important improvement to the traditional algorithm. Experimental results show that the SC-DQN algorithm can help UH avoid the moving obstacles to unknown motion status in the environment safely and effectively and successfully complete the raid task.

Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an automatic robust infrared and visible image registration algorithm, based on a deep convolutional network, was proposed. In order to precisely search and locate the feature points, a deep convolutional network is introduced, which solves the problem that a large number of feature points can still be extracted when the pixels of the infrared image are not clear. Then, in order to achieve accurate feature point matching, a rough-to-fine matching algorithm is designed. The rough matching is obtained by location orientation scale transform Euclidean distance, and then, the fine matching is performed based on the update global optimization, and finally, the image registration is realized. Experimental results show that the proposed algorithm has better robustness and accuracy than several advanced registration algorithms.