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首页> 外文会议>IEEE/ION Position, Location and Navigation Symposium >Q-Learning Applied to Soft-Kill Countermeasures For Unmanned Aerial Vehicles (UAVs)
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Q-Learning Applied to Soft-Kill Countermeasures For Unmanned Aerial Vehicles (UAVs)

机译：Q学习在无人飞行器（UAV）软杀对策中的应用

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摘要

This work presents a three-dimensional control algorithm using reinforcement learning to guide an attacking hunter drone capable of performing a global navigation satellite systems (GNSS) repeater attack on the GNSS receiver of a target invader drone. Considering the mission and movement requirements of the hunter drone, a Q-learning algorithm was developed, for which the table with the possible transitions of the states and actions is obtained by the actions that the vehicle can take considering directions and the respective consequences of each action. The learning capability of the proposed algorithm arises from the trial and error by an agent. The penalty calculation is based on the error of the invader position to the hunter's desired position of the attacked drone. The developed algorithm is tested using a software-in-the-loop (SITL) implementation, which is based on the Ardupilot platform. SITL simulations are performed in a developed testbed to emulate operational scenarios, where an unmanned aerial vehicle (UAV) is hijacked and then controlled by an attacking UAV until it reaches the final position desired by the hunter, usually a secure area where the vehicle can be captured without being destroyed. Results, including error metrics and action time, are discussed for different mission scenarios.
机译：这项工作提出了一种使用强化学习的三维控制算法，以指导攻击的猎人无人机能够对目标入侵者无人机的GNSS接收器执行全球导航卫星系统（GNSS）中继器攻击。考虑到猎人无人机的任务和移动要求，开发了一种Q学习算法，针对该算法，状态和动作的可能转换表是通过车辆可以考虑方向的动作以及每种动作各自的后果而获得的行动。所提出算法的学习能力来自于代理人的反复试验。惩罚计算是基于入侵者位置与被攻击无人机的猎人期望位置之间的误差。使用基于Ardupilot平台的软件在环（SITL）实施方案对开发的算法进行了测试。在已开发的测试平台上进行SITL仿真，以模拟操作场景，其中劫持无人飞行器（UAV），然后由攻击的无人机控制，直到到达猎人所需的最终位置，通常是可以将飞行器安放于其上的安全区域被俘而没有被摧毁。讨论了针对不同任务场景的结果，包括错误指标和行动时间。

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