集成强化学习算法的无人机惯性导航技术研究
Research on Inertial Navigation Technology of Unmanned Aerial Vehicles with Integrated Reinforcement Learning Algorithm
AI总结：
本研究提出一种基于强化学习的DQM算法，通过设计状态空间与调整机制优化无人机惯性导航系统，有效降低了动态环境下的累积误差，为无人机自主导航奠定了基础。
摘要：
我们首先定义了合适的状态表示和动作空间，然后基于智能体选择的动作设计了一种调整机制。该调整机制输出智能体的下一个状态和奖励值。此外，调整机制计算调整后的状态与未调整状态之间的误差。进一步地，智能体将包含状态和奖励值的经验样本存储在缓冲区中，并在每次迭代时重放这些经验，以学习环境的动态特性。我们将改进后的算法命名为DQM算法。实验结果表明，使用我们提出算法的智能体有效减少了动态环境中惯性导航的累积误差。尽管我们的研究为实现无人机自主导航提供了基础，但仍有很大的优化空间。进一步的研究可以包括在模拟环境中测试无人机、在真实环境中测试无人机、优化奖励函数的设计、改进算法工作流程以提升收敛速度和性能，以及增强算法的泛化能力。
英文摘要：
We first define appropriate state representation and action space, and then design an adjustment mechanism based on the actions selected by the intelligent agent. The adjustment mechanism outputs the next state and reward value of the agent. Additionally, the adjustment mechanism calculates the error between the adjusted state and the unadjusted state. Furthermore, the intelligent agent stores the acquired experience samples containing states and reward values in a buffer and replays the experiences during each iteration to learn the dynamic characteristics of the environment. We name the improved algorithm as the DQM algorithm. Experimental results demonstrate that the intelligent agent using our proposed algorithm effectively reduces the accumulated errors of inertial navigation in dynamic environments. Although our research provides a basis for achieving autonomous navigation of unmanned aerial vehicles, there is still room for significant optimization. Further research can include testing unmanned aerial vehicles in simulated environments, testing unmanned aerial vehicles in real-world environments, optimizing the design of reward functions, improving the algorithm workflow to enhance convergence speed and performance, and enhancing the algorithm's generalization ability.
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。
