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首页> 外文期刊>Procedia Computer Science >Three Dimensional Path Planning for UAVs in Dynamic Environment using Glow-worm Swarm Optimization
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Three Dimensional Path Planning for UAVs in Dynamic Environment using Glow-worm Swarm Optimization

机译：萤火虫群优化在动态环境下无人机的三维路径规划

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We propose an efficient solution for finding a collision-free path in a Three-Dimensional environment with dynamic obstacles for Unmanned Aerial Vehicles (UAVs). Path Planning for Unmanned Aerial Vehicles (UAVs) in Three Dimensional Dynamic Environment is considered a challenging task in the field of robotics. During their mission, UAVs have to maneuver in an environment which can have obstacles of varying size and random motion. The aim of the proposed algorithm is to traverse an optimal flight route in real world environment with no collision with environmental elements. This paper proposes use of a Glow-worm Swarm Optimization (GSO) for Path-Planning of Unmanned Aerial Vehicles (UAVs). It provides improved convergence rate and accuracy than the other Meta Heuristic optimization algorithms. The simulation is modelled in a real world environment. A swarm of particles is made to co-ordinate with each other for optimal path planning. The simulation is run in Python and the viability of the algorithm according to path-cost, time and number of expanded nodes is measured.
机译：我们提出了一种有效的解决方案，用于在无人飞行器（UAV）具有动态障碍的三维环境中寻找无碰撞路径。在三维动态环境中的无人飞行器（UAV）路径规划被认为是机器人技术领域的一项艰巨任务。在执行任务期间，无人机必须在可能具有大小变化和随机运动的障碍物的环境中进行机动。提出的算法的目的是在真实环境中遍历最佳飞行路线，而不会与环境元素发生冲突。本文提出了使用萤火虫虫群优化（GSO）进行无人机计划（UAV）的路径规划。与其他Meta Heuristic优化算法相比，它提供了更高的收敛速度和准确性。模拟是在现实环境中建模的。使一组粒子相互协调以实现最佳路径规划。仿真是在Python中运行的，并且根据路径成本，时间和扩展节点的数量来衡量算法的可行性。

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