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:多个无人驾驶航空飞行器(无人驾驶飞行器)的路线规划是一系列翻译和轮换步骤,从一个特定起始地点到目的地目标地点。路线规划问题的目标是确定避免与环境中存在的障碍发生任何碰撞的最理想路线。路线规划是一个NP硬性优化问题。在本文中,新提议的Salp Swararm Algorithm(SSA)被使用,其性能与确定性和其他自然引发的阿尔高利差进行比较。结果显示,在3D环境中多架无人驾驶飞行器的路线规划中,SSA超过所有其他超重性算法。与最近报告的数据相比,拟议方法将平均成本和总时间分别提高1.25%和6.035%。线路规划涉及许多实际应用,如机器人导航、自驾驶汽车、自动无人驾驶无人驾驶飞行器等,用于危险地面-零情况下的搜索和救援行动、民用监视、军事作战甚至商业服务,如无人驾驶飞机的包裹。