基于Salp Swarm Algorithm的多无人机三维路径规划
Three Dimensional Route Planning for Multiple Unmanned Aerial Vehicles using Salp Swarm Algorithm
AI总结：
针对多无人机三维避障路径规划这一NP难问题，本文采用SSA并与多种算法比较，证明其在平均代价和总时间上分别提升1.25%和6.035%。
摘要：
多架无人机（UAV）的路径规划是从给定起始位置到目标终点位置的一系列平移与旋转步骤。路径规划问题的目标是确定一条最优路径，避免与环境中存在的障碍物发生任何碰撞。路径规划是一个NP难优化问题。本文采用新近提出的Salp Swarm Algorithm（SSA，樽海鞘群算法），并将其性能与确定性算法及其他自然启发算法（NIAs）进行比较。结果表明，在三维环境中为多架UAV进行路径规划时，SSA优于所有其他元启发式算法。与近期报告的数据相比，所提方法将平均代价和总时间分别改善了1.25%和6.035%。路径规划涉及许多现实应用，例如机器人导航、自动驾驶汽车、在危险的爆心现场执行搜救任务的自主UAV、民用监视、军事作战，甚至包括无人机包裹递送等商业服务。
英文摘要：
Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of translation and rotational steps from a given start location to the destination goal location. The goal of the route planning problem is to determine the most optimal route avoiding any collisions with the obstacles present in the environment. Route planning is an NP-hard optimization problem. In this paper, a newly proposed Salp Swarm Algorithm (SSA) is used, and its performance is compared with deterministic and other Nature-Inspired Algorithms (NIAs). The results illustrate that SSA outperforms all the other meta-heuristic algorithms in route planning for multiple UAVs in a 3D environment. The proposed approach improves the average cost and overall time by 1.25% and 6.035% respectively when compared to recently reported data. Route planning is involved in many real-life applications like robot navigation, self-driving car, autonomous UAV for search and rescue operations in dangerous ground-zero situations, civilian surveillance, military combat and even commercial services like package delivery by drones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
