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.

翻译：路径规划对于无人机至关重要，它决定了无人机完成任务所需遵循的飞行轨迹。本研究通过提出一种名为基于导航变量的多目标粒子群优化算法来解决该问题。该算法首先通过定义一组包含无人机运行最优性与安全性要求的目标函数，将路径规划建模为优化问题。随后，NMOPSO 算法通过帕累托最优解来最小化这些目标函数。该算法的特点在于采用基于导航变量的新型路径表示方法，以纳入运动学约束并充分利用无人机的机动特性。同时，算法引入了自适应变异机制以增强种群多样性，从而获得更优解。通过与多种算法进行比较，对所提方法进行了基准测试。结果表明，NMOPSO 不仅优于其他粒子群优化变体，也优于其他先进的多目标与元启发式优化算法。研究还通过真实无人机实验验证了该方法在实际飞行中的有效性。算法源代码发布于 https://github.com/ngandng/NMOPSO。