Three Dimensional Route Planning for Multiple Unmanned Aerial Vehicles using Salp Swarm Algorithm
This addresses route planning for UAVs in applications like search and rescue and surveillance, but it is incremental as it applies a new algorithm to an existing problem.
The paper tackled the NP-hard problem of 3D route planning for multiple UAVs by applying the Salp Swarm Algorithm, which outperformed other meta-heuristic algorithms, improving average cost by 1.25% and overall time by 6.035% compared to recent data.
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.