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.

翻译：多无人机航迹规划是指从给定起始位置到目标位置的一系列平移与旋转步骤。航迹规划问题的目标是确定最优航迹，同时避免与环境中的障碍物发生碰撞。航迹规划是一个NP难优化问题。本文采用新提出的樽海鞘群算法，并将其性能与确定性算法及其他自然启发算法进行比较。结果表明，在三维环境下多无人机航迹规划中，SSA优于所有其他元启发式算法。与近期文献报道的数据相比，所提方法在平均成本和总时间上分别提升了1.25%和6.035%。航迹规划广泛应用于诸多现实场景，例如机器人导航、自动驾驶汽车、用于危险零地况搜索救援任务的自主无人机、民用监控、军事作战，甚至包括无人机快递包裹投递等商业服务。