用于无人机导航的混合人工智能策略
Hybrid Artificial Intelligence Strategies for Drone Navigation
AI总结：
本文提出融合强化学习深度学习模型与基于规则引擎的无人机混合导航策略，支持决策解释和人类参与，并通过多场景评估验证其在任务完成、防撞和目标定位效率上的提升。
摘要：
目标：本文描述了用于无人机导航的混合人工智能策略的开发。方法：该导航模块根据智能体状态，将深度学习模型与基于规则的引擎相结合。深度学习模型已使用强化学习进行训练。基于规则的引擎利用专家知识来处理特定情况。导航模块融合了若干策略，以基于无人机的观测空间解释其决策，并采用不同机制将人类决策纳入导航过程。最后，本文提出了一种评估方法，该方法基于定义若干场景，并根据适配每个场景的指标分析不同策略的性能。结果：研究了两个主要导航问题。对于第一个场景（到达已知目标），已能够获得90%的任务完成率，并且得益于基于规则的引擎，显著减少了碰撞次数。对于第二个场景，使用强化学习模型已能够将定位所有目标所需的时间减少20%。结论：强化学习是学习无人机导航策略的一种非常好的策略，但在关键情况下，有必要用基于规则的模块对其进行补充，以提高任务成功率。
英文摘要：
Objective: This paper describes the development of hybrid artificial intelligence strategies for drone navigation. Methods: The navigation module combines a deep learning model with a rule-based engine depending on the agent state. The deep learning model has been trained using reinforcement learning. The rule-based engine uses expert knowledge to deal with specific situations. The navigation module incorporates several strategies to explain the drone decision based on its observation space, and different mechanisms for including human decisions in the navigation process. Finally, this paper proposes an evaluation methodology based on defining several scenarios and analyzing the performance of the different strategies according to metrics adapted to each scenario. Results: Two main navigation problems have been studied. For the first scenario (reaching known targets), it has been possible to obtain a 90% task completion rate, reducing significantly the number of collisions thanks to the rule-based engine. For the second scenario, it has been possible to reduce 20% of the time required to locate all the targets using the reinforcement learning model. Conclusions: Reinforcement learning is a very good strategy to learn policies for drone navigation, but in critical situations, it is necessary to complement it with a rule-based module to increase task success rate.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
