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

Abstract

IntroductionCurrent 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.MethodsThis 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.ResultsThe simulation results of the three test fields show that compared with ACO, the path length optimization rate of MS-ACO are 3.89%, 4.6% and 2.86%, respectively, the optimization rate of total path angles are 21.94%, 45.06% and 55.94%, respectively, and the optimization rate of node numbers are 61.05%, 74.84% and 75.47%, respectively. 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%, the optimization rate of flight time are 46.67%, 56% and 59.01%, respectively, and the optimization rate of corner angle are 50.76%, 61.78% and 71.1%, respectively.DiscussionThe feasibility and effectiveness of the algorithm were further verified. The algorithm proposed in this study can optimize the spraying path according to the position of each fruit tree and the flight characteristics of UAV, 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.

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm

“…(2022) used 3D and 2D LiDAR to map greenhouse orchards and used the Dijkstra algorithm to plan global paths for mobile robots. Tian et al. (2023) proposed an ant colony optimization algorithm to solve multitarget waypoint planning for unmanned aerial vehicles in orchards.…”

“…Jiang et al (2022) used 3D and 2D LiDAR to map greenhouse orchards and used the Dijkstra algorithm to plan global paths for mobile robots. Tian et al (2023) proposed an ant colony optimization algorithm to solve multitarget waypoint planning for unmanned aerial vehicles in orchards. Zhang et al (2023) introduced an improved artificial potential field algorithm for weed-removal robot path planning, improving the safety of robot automation operations.…”

Optimal ant colony algorithm for UAV airborne LiDAR route planning in densely vegetated areas

“…And the ant colony algorithm, as a classical path algorithm, has been improved by many scholars in terms of its search speed, planning accuracy, and operational stability. Li et al 11 proposed an improved ant colony algorithm to solve the multi-UAV trajectory planning problem in a static environment in response to the problems of slow convergence, low search efficiency, and collision between UAVs and obstacles in the existing UAV path planning algorithm; Konatowski 12 used an ant colony algorithm to autonomously construct the optimal UAV route, which establishes the spatial orientation of the UAV and indicates its transition direction for each intermediate waypoint; Tian et al 13 performed multi-objective node optimization by improving the heuristic function in ant colony optimization, an algorithm that combines corner cost and distance cost. It effectively reduces the energy consumption of UAV flight and improves the operation efficiency; Liu et al 14 introduced the concepts of iteration period and reinforcement in the pheromone update rule of ant colony optimization (ACO) to improve the convergence accuracy and global optimization ability, and an ant colony binary iteration optimization is proposed.…”

“…Li et al 11 . proposed an improved ant colony algorithm to solve the multi-UAV trajectory planning problem in a static environment in response to the problems of slow convergence, low search efficiency, and collision between UAVs and obstacles in the existing UAV path planning algorithm; Konatowski 12 used an ant colony algorithm to autonomously construct the optimal UAV route, which establishes the spatial orientation of the UAV and indicates its transition direction for each intermediate waypoint; Tian et al 13 . performed multi-objective node optimization by improving the heuristic function in ant colony optimization, an algorithm that combines corner cost and distance cost.…”