论文标题
无人机自动导航，映射和目标检测的强化学习
Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection
论文作者
论文摘要
在本文中，我们研究了配备有低复杂性雷达并在未知环境中飞行的单一无人机（UAV）的联合检测，映射和导航问题。目的是以最大化映射准确性来优化其轨迹，同时避免从目标检测的角度来看测量可能不足以提供信息的领域。该问题被称为马尔可夫决策过程（MDP），在该过程中，无人机是运行州估计器进行目标检测和环境映射的代理，以及增强学习（RL）算法来推断其自己的导航政策（即控制法）。数值结果表明，提出的想法的可行性，强调了无人机在重建周围环境时自主探索具有高可能性检测可能性的区域的能力。
In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a reinforcement learning (RL) algorithm to infer its own policy of navigation (i.e., the control law). Numerical results show the feasibility of the proposed idea, highlighting the UAV's capability of autonomously exploring areas with high probability of target detection while reconstructing the surrounding environment.
