  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.
