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We first define appropriate state representation and action space, and thendesign an adjustment mechanism based on the actions selected by the intelligentagent. The adjustment mechanism outputs the next state and reward value of theagent. Additionally, the adjustment mechanism calculates the error between theadjusted state and the unadjusted state. Furthermore, the intelligent agentstores the acquired experience samples containing states and reward values in abuffer and replays the experiences during each iteration to learn the dynamiccharacteristics of the environment. We name the improved algorithm as the DQMalgorithm. Experimental results demonstrate that the intelligent agent usingour proposed algorithm effectively reduces the accumulated errors of inertialnavigation in dynamic environments. Although our research provides a basis forachieving autonomous navigation of unmanned aerial vehicles, there is stillroom for significant optimization. Further research can include testingunmanned aerial vehicles in simulated environments, testing unmanned aerialvehicles 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.

Research on Inertial Navigation Technology of Unmanned Aerial Vehicles with Integrated Reinforcement Learning Algorithm