This work presents a novel path-planning approach for Unmanned Aerial Vehicles (UAVs) in continuous 3D environments. This proposal aims to minimize the path length while avoiding collisions through the suitable adjusting of control points (the points that take the UAV from a start position to a target location). The above is stated as a constrained global optimization problem. This problem considers the overall length of the path as the single objective function. Regarding the problem constraints, they are related to the collision of the obstacles with the 3D shape of a path. The assignment of the path shape is also proposed in this work to streamline the planning process. Due to the optimization problem features (high nonlinearity, multimodality, non-differentiability, and the lack of an initial guess solution), a constraints-handling mechanism is used in meta-heuristics to find suitable optimized paths. Also, an enhanced path-search mechanism is included in these algorithms to deal with complex planning scenarios. The enhanced mechanism incorporates a path computed by a variant of the A-Star method (the Pruned A-Star) in the first set of candidate solutions of the meta-heuristics. The proposed approach is tested through six complex scenarios. Moreover, the performance of three well-known meta-heuristics, Differential Evolution (DE), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA), is studied to find a potential candidate to solve the path-planning problem. In this way, the paths found by DE show outstanding performance. The paths obtained by the Pruned A-Star technique are adopted as a point of comparison to determine the advantages and drawbacks of the proposal.

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 招募会员


这项工作为连续 3D 环境中的无人机 (UAV) 提出了一种新颖的路径规划方法。该提案旨在通过适当调整控制点（将无人机从起始位置带到目标位置的点）来最小化路径长度，同时避免碰撞。以上被描述为约束全局优化问题。该问题将路径的总长度视为单一目标函数。关于问题约束，它们与障碍物与路径的 3D 形状的碰撞有关。这项工作还提出了路径形状的分配，以简化规划过程。由于优化问题的特点（高度非线性、多模态、不可微分、缺乏初始猜测解），元启发式中使用约束处理机制来寻找合适的优化路径。此外，这些算法中还包含增强的路径搜索机制，以处理复杂的规划场景。增强的机制将由 A-Star 方法的变体（修剪的 A-Star）计算的路径合并到元启发式的第一组候选解决方案中。所提出的方法通过六个复杂场景进行了测试。此外，还研究了三种著名的元启发式算法：差分进化（DE）、粒子群优化（PSO）和遗传算法（GA）的性能，以找到解决路径规划问题的潜在候选算法。这样，DE发现的路径就表现出了出色的性能。采用剪枝 A-Star 技术获得的路径作为比较点，以确定该提案的优点和缺点。