A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

Liu, Yangyang; Zhang, Pengyang; Yu, Ruan; Wu, Duanqin; Wang, Shunli; Yin, Niuniu; Meng, Fanbin; Liu, Zhongcheng

doi:10.3389/fpls.2022.998962

Cited by 10 publications

(10 citation statements)

References 46 publications

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“…In orchard areas with obvious height differences, elevation and tree height are one of the important factors affecting spraying efficiency. In the vast area of hilly mountainous terrain in China, orchard and tea gardens are commonly planted along the slopes ( Wang et al., 2019 ; Liu et al., 2022 ). The path planning based on MS-ACO has more application value for such scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…This algorithm has good problem optimization ability, and is easy to combine with other algorithms, and is widely used to solve optimization problems such as traveling salesman and secondary allocation of resources (Ebadinezhad, 2020;Zhang et al, 2021b). For example, Liu et al (2022) realized the plant protection route planning for many tea fields in hilly areas by combining genetic algorithm and ant colony algorithm. Cao et al (2021) added the influence of agricultural machinery operation execution ability to the pheromone update mechanism of the ant colony algorithm, and realized the collaborative management of agricultural machinery.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tian

et al. 2023

Front. Plant Sci.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tian

et al. 2023

Front. Plant Sci.

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“…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. The proposed algorithm maintains advantages in performance and stability when solving standard traveling salesman problems with more complex objectives, as well as the planning accuracy and search speed.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Tang,

Li,

et al. 2023

J. Appl. Rem. Sens.

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.In order to solve the problems of redundant data acquisition and sparse ground points in dense vegetation areas by conventional unmanned aerial vehicle (UAV) path planning methods, an UAV-airborne LiDAR route optimization method for dense vegetation areas is proposed. First, based on the high-resolution true color remote sensing images of the study area, the “fuzzy” calculation of vegetation coverage for route planning is completed. Then, an optimized ant colony algorithm is proposed for route planning, which introduces vegetation coverage as a reference for route planning and optimizes the pheromone initialization, state transfer rules, pheromone calculation, and update strategies in the classical ant colony algorithm to obtain more ground points. The experimental results show that this method can take into account the vegetation coverage of the flight area and find the area with low vegetation coverage to complete the route planning and efficiently use the sweeping principle of three-dimensional laser scanning to improve the probability of ground point acquisition, with faster iteration speed than the classical ant colony algorithm, and improve the efficiency of ground point acquisition in dense vegetation areas.

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Path planning for intelligent vehicles based on improved D* Lite

Wang

et al. 2023

J Supercomput

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Typical path planning algorithms are good for static obstacles avoidance, but not for dynamic obstacles, so path planning of intelligent vehicles in uncharted regions is a fundamental and critical problem. This study suggests an improved D* Lite path planning algorithm to address the issues of large corner, node redundancy and close to obstacles in the path planned by D* Lite algorithm. Firstly, in order to increase the safety of the path, the D* Lite algorithm sets the safety distance between the intelligent vehicle and obstacles. Then, the kinematic constraints of intelligent vehicles are introduced to increase the path search direction and avoid path corners exceeding the steering maneuverability of intelligent vehicles. Next, the path is optimized, and the optimization process of removing redundant points is employed to tackle the problem of curved search path and redundant nodes, and the path is smoothed by using third-order Bezier curve to generate a path with continuous curvature. Finally, the enhanced D* Lite algorithm is fused with the improved dynamic window approach (IDWA) to achieve real-time obstacle avoidance based on the global optimal path for moving obstacles. Simulation studies in static and dynamic contexts are used to demonstrate the usefulness of the revised D* Lite algorithm. The results show that compared with other path planning methods, the path generated by the proposed method has more safety and smoothness features, and improves the path quality. Therefore, the proposed algorithm has certain effectiveness and superiority in path planning problems in static and dynamic environments.

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A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

Abstract: F and Liu Z (2022) A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.

Cited by 10 publications

References 46 publications

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

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

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

Path planning for intelligent vehicles based on improved D* Lite

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