Optimizing UAV Routes: An Implementation and Evaluation of Ant-Colony Optimization Algorithm on Crazyflie Quadcopter for Solving the Traveling Salesman Problem

Abstract

Abstract The Traveling Salesman Problem (TSP) involves optimizing a route to find the most efficient path. In agricultural scenarios, a practical example of TSP arises when Unmanned Aerial Vehicles (UAVs) are required to traverse multiple locations (nodes) to execute specific tasks such as monitoring or fertilization. One of the algorithms employed for solving TSP is Ant-Colony Optimization (ACO). The ACO algorithm operates by utilizing the “ants” as the virtual agents exploring the potential routes and storing the information in memory to determine the optimal route. This research aims to address the TSP problem using the ACO algorithm and subsequently apply it to the Crazyflie quadcopter. The developed ACO algorithm is designed to identify the most efficient route, guiding the UAV along the obtained path. Test results demonstrate the successful navigation of the Crazyflie quadcopter to the specified points, with mean error of 0.02 meters, 0.02 meters, and 0.01 meters on the x-, y- and z-axes respectively.