标题： 基于导航变量的多目标粒子群优化算法用于具有运动学约束的无人机路径规划 显示英文标题

标题： Navigation Variable-based Multi-objective Particle Swarm Optimization for UAV Path Planning with Kinematic Constraints

摘要： 路径规划对于无人驾驶飞行器（UAVs）至关重要，因为它决定了UAV需要遵循的路径以完成任务。 本工作通过引入一种称为基于导航变量的多目标粒子群优化（NMOPSO）的新算法来解决这个问题。 它首先通过定义一组目标函数将路径规划建模为优化问题，这些目标函数包括UAV操作的最优性和安全性要求。 然后使用NMOPSO通过帕累托最优解来最小化这些函数。 该算法基于导航变量的新路径表示，以包含运动学约束并利用UAV的机动特性。 它还包括一种自适应变异机制，以增强群体的多样性，从而获得更好的解决方案。 已与各种算法进行了比较，以对所提出的方法进行基准测试。 结果表明，NMOPSO不仅优于其他粒子群优化变体，还优于其他最先进的多目标和元启发式优化算法。 还进行了与真实UAV的实验，以确认该方法在实际飞行中的有效性。 该算法的源代码可在 https://github.com/ngandng/NMOPSO 获取。 显示英文摘要

摘要： Path planning is essential for unmanned aerial vehicles (UAVs) as it determines the path that the UAV needs to follow to complete a task. This work addresses this problem by introducing a new algorithm called navigation variable-based multi-objective particle swarm optimization (NMOPSO). It first models path planning as an optimization problem via the definition of a set of objective functions that include optimality and safety requirements for UAV operation. The NMOPSO is then used to minimize those functions through Pareto optimal solutions. The algorithm features a new path representation based on navigation variables to include kinematic constraints and exploit the maneuverable characteristics of the UAV. It also includes an adaptive mutation mechanism to enhance the diversity of the swarm for better solutions. Comparisons with various algorithms have been carried out to benchmark the proposed approach. The results indicate that the NMOPSO performs better than not only other particle swarm optimization variants but also other state-of-the-art multi-objective and metaheuristic optimization algorithms. Experiments have also been conducted with real UAVs to confirm the validity of the approach for practical flights. The source code of the algorithm is available at https://github.com/ngandng/NMOPSO.