This paper addresses a path planning problem for unmanned aerial vehicles with correcting position errors through correction-point navigation, which requires a rapid response when determining the flight path. A two-layer nested iterative hybrid algorithm based on $Q$ learning is proposed to achieve multiobjective optimization by minimizing path lengths and correction times while reducing the complexity of the algorithm and improving the efficiency of path planning. The lower-layer algorithm uses a $Q$-learning framework based on the experience playback mechanism and exploration/exploitation mechanism. The rewards and punishments for the $Q$-table values in the lower-layer algorithm are innovatively managed with the unit “path.” The upper-layer algorithm is based on the Pareto multiobjective algorithm. The Pareto frontier is continuously updated with the solutions from the lower-layer algorithm, which timely provides the lower-layer algorithm feedback. Finally, simulation experiments are conducted to evaluate the effectiveness of the algorithm, and the results show that the proposed algorithm outperforms particle swarm optimization.

本文针对通过校正点导航校正位置误差的无人飞行器的路径规划问题，在确定飞行路径时需要快速响应。基于两层嵌套迭代混合算法$问$通过最小化路径长度和校正时间，同时降低算法的复杂性并提高路径规划的效率，提出了一种通过学习来实现多目标优化的方法。下层算法使用$问$-基于经验回放机制和探索/开发机制的学习框架。的奖惩$问$使用“路径”单位对下层算法中的表值进行创新管理。上层算法基于帕累托多目标算法。使用低层算法的解决方案不断更新帕累托边界，从而及时提供低层算法的反馈。最后，通过仿真实验评估了算法的有效性，结果表明该算法优于粒子群算法。