用导航变量优化无人机路径，兼顾飞行安全与效率。
Navigation Variable-based Multi-objective Particle Swarm Optimization for UAV Path Planning with Kinematic Constraints
- 基于导航变量表示路径，融入飞行器运动约束。
- 在多个真实场景测试中优于主流优化算法。
- 适合需高机动性路径规划的无人机任务。
路径规划对无人机完成任务至关重要，本文提出一种基于导航变量的多目标粒子群优化算法（NMOPSO），将路径规划建模为包含最优性与安全性要求的多目标优化问题，并通过帕累托最优解进行求解。该算法采用基于导航变量的新路径表示方法，有效融入飞行器的运动学约束，充分发挥其机动性能；同时引入自适应变异机制，增强群体多样性以获得更优解。与多种粒子群优化变体及其他先进多目标与元启发式算法对比表明，NMOPSO表现更优。此外，在真实无人机上进行了实验验证，确认该方法适用于实际飞行任务。算法源码已开源：https://github.com/ngandng/NMOPSO。
原文摘要 · Abstract (English)
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.
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