视觉感知的无人机端到端目标跟踪控制技术

针对无人机机动目标跟踪的自主运动控制问题，提出连续型动作输出的无人机端到端主动目标跟踪控制方法. 设计基于视觉感知和深度强化学习策略的端到端决策控制模型，将无人机观察的连续帧视觉图像作为输入状态，输出无人机飞行动作的连续型控制量. 为了提高控制模型的泛化能力，改进基于任务分解和预训练的高效迁移学习策略. 仿真结果表明，该方法能够在多种机动目标跟踪任务中实现无人机姿态的自适应调整，使得无人机在空中能够稳定跟踪移动目标，显著提高了无人机跟踪控制器在未知环境下的泛化能力和训练效率.

Vision-driven end-to-end maneuvering object tracking of UAV

An end-to-end active object tracking control method of UAV with continuous motion output was proposed aiming at the autonomous motion control problem of UAV maneuvering object tracking. An end-to-end decision-making control model based on visual perception and deep reinforcement learning strategy was designed. The continuous visual images observed by UAV were taken as the input state, and the continuous control quantity of UAV flight action was output. An efficient transfer learning strategy based on task decomposition and pre training was proposed in order to improve the generalization ability of control model. The simulation results show that the method can realize the adaptive adjustment of UAV attitude in a variety of maneuvering object tracking tasks and make the UAV stably track the moving object in the air. The generalization ability and training efficiency of UAV tracking controller in unknown environment were significantly improved.