Ant Colony Optimization Based UAV Path Planning for Autonomous Agricultural Spraying

• Published in: 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) pp. 910 - 916
• Main Author: Zheng, Haolong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18-11-2022
• Subjects: advanced ant colony optimization; agriculture robotics; Ant colony optimization; Autonomous aerial vehicles; autonomous spraying; Energy consumption; Linear programming; path planing; Simulation; Spraying; Traveling salesman problems; Unmanned aerial vehicle (UAV)
• ISSN: 2831-4549
• DOI: 10.1109/AUTEEE56487.2022.9994402

The use of unmanned aerial vehicles (UAVs) for pesticide spraying in agricultural settings is growing rapidly. In this context, path planning directly affects the efficiency of the system. However, the most recently used method failed to fully exploit the maneuverability and energy of UAVs. This study begins by introducing a model of energy consumption, from which an objective function based on energy is derived. Then, considering the spraying constraint of the real scenario, the problem is finally simplified as a variation multiple traveling salesman problem (mTSP). Explicitly, an Ant Colony Optimization (ACO) based algorithm is conceived for solving the problem. The simulation outcome is provided to demonstrate the effectiveness of our algorithm.