AUTHOR=Tian Haoxin , Mo Zhenjie , Ma Chenyang , Xiao Junqi , Jia Ruichang , Lan Yubin , Zhang Yali 

TITLE=Design and validation of a multi-objective waypoint planning algorithm for UAV spraying in orchards based on improved ant colony algorithm

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1101828

DOI=10.3389/fpls.2023.1101828

ISSN=1664-462X

ABSTRACT=Current aerial plant protection with Unmanned Aerial Vehicles (UAV) usually applies full coverage route planning, which is challenging for plant protection operations in the orchards in South China. Because the fruit planting has the characteristics of dispersal and irregularity, full-coverage route spraying causes re-application as well as missed application, resulting in environmental pollution. Therefore, it is of great significance to plan an efficient, low-consumption and accurate plant protection route considering the flight characteristics of UAVs and orchard planting characteristics. This study proposes a plant protection route planning algorithm to solve the waypoint planning problem of UAV multi-objective tasks in orchard scenes. By improving the heuristic function in Ant Colony Optimization (ACO), the algorithm combines corner cost and distance cost for multi-objective node optimization. At the same time, a sorting optimization mechanism was introduced to speed up the iteration speed of the algorithm and avoid the influence of inferior paths on the optimal results. Finally, Multi-source Ant Colony Optimization (MS-ACO) was proposed after cleaning the nodes of the solution path. The simulation shows that compared with ACO, the optimization rate of MS-ACO's path length is 2.86%~3.89%, the optimization rate of total path angle is 21.94%~55.94%, and the optimization rate of node number is 61.05%~74.84%. MS-ACO can effectively reduce the corner cost and the number of nodes. The results of field experiments show that for each test field, MS-ACO has a significant optimization effect compared with ACO, with an optimization rate of energy consumption per meter of more than 30%, an optimization rate of flight time of 46.67%~59.01%, and an optimization rate of corner angle of 50.76%~71.1%. The feasibility and effectiveness of the algorithm were further verified. The algorithm proposed in this study can effectively reduce the energy consumption of UAV flight, improve the operating efficiency, and provide technical reference for the waypoint planning of plant protection UAV in the orchard scene.