基于人工智能的无人机区域侦察方法研究现状与发展

Status and Development of Regional Reconnaissance Methods of UAV Based on Artificial Intelligence

区域侦察是无人机研究领域的一个重要分支。由于实际任务和环境十分复杂,区域侦察控制方法必须具备较快的计算速度、较强的自主性和智能性。人工智能因具有学习能力强、效率高、融合度高等特性被应用于区域侦察任务中。本文系统介绍了区域侦察问题的背景并综述了基于人工智能解决该问题的方法,主要分为构造并优化目标函数的启发式算法和求解最优价值或策略的深度强化学习方法这两类。通过对上述方法的全方位比较,发现深度强化学习因具有自学习和在线学习的性能,能很好地适应复杂、未知环境进而能快速、准确解决区域侦察问题。此外,本文还探讨了无人机区域侦察技术的发展趋势及深度强化学习面临的挑战。

Regional reconnaissance is an important branch of unmanned aerial vehicle(UAV) research. Due to the complexity of the actual mission and environment, the control method of regional reconnaissance must be provided with fast calculation speed, strong autonomy and intelligence. Artificial intelligence has been used in regional reconnaissance because of its strong learning ability, high efficiency, and high degree of integration. This paper systematically introduces the background of the regional reconnaissance problem and summarizes the methods based on artificial intelligence to solve this problem, which are mainly divided into two categories: heuristic algorithms for constructing and optimizing the objective function and deep reinforcement learning methods for solving the optimal value or strategy. Given by a comprehensive comparison of the above methods, it is found that deep reinforcement learning performs self-learning and online learning well, which can adapt to complex and unknown environments,and further it can quickly and accurately solve regional reconnaissance problems. In addition, this paper also discusses the development trend of regional reconnaissance technology and the challenges faced by deep reinforcement learning.