Whole area coverage strategy of agricultural robot based on adaptive heating simulated annealing algorithm

WANG Wei; ZHANG Yanfei; GONG Jinliang; LAN Yubin

doi:10.7671/j.issn.1001-411X.202104022

Journal of South China Agricultural University > 2021 > 42(6): 126-132. > DOI: 10.7671/j.issn.1001-411X.202104022

WANG Wei, ZHANG Yanfei, GONG Jinliang, et al. Whole area coverage strategy of agricultural robot based on adaptive heating simulated annealing algorithm[J]. Journal of South China Agricultural University, 2021, 42(6): 126-132. DOI: 10.7671/j.issn.1001-411X.202104022

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Whole area coverage strategy of agricultural robot based on adaptive heating simulated annealing algorithm

1.
School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China
2.
School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
3.
College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China

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Received Date: April 20, 2021
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
To propose a whole area coverage strategy of agricultural robot in complex farmland environment, and reasonably plan the working traversal path of agricultural robot.

Method
The complex farmland working environment model was defined according to the actual production environment of agricultural robot, and the concepts of first-level partition and second-level partition were established. The idea of genetic algorithm mutation operation was introduced to establish a high-quality feasible solution generation method of simulated annealing algorithm based on greedy mechanism. Based on the establishment of the concept of solution set diversity, an improved method of simulated annealing algorithm based on adaptive heating was designed to solve the problem of the optimal traversal sequence between partitions. The A* algorithm was combined with the eight-neighbor search method to plan the cross-regional connection path of agricultural robot. By this way, the scheme designed in this paper could achieve that the robot covered the whole working area.

Result
The simulation results showed that, compared with the traditional genetic algorithm and simulated annealing algorithm, the path length planned by the improved simulated annealing algorithm was reduced by 14.7% and 10.1% respectively, and the number of iterations during convergence was reduced by 9.8% and 59.1% respectively. The repeating rate of the traversal path of the agricultural robot in the simulation test of whole area coverage was 14.86%. The path repetition rate in the field traversal test of the high ground-clearance spraying robot was 15.83%.

Conclusion
The research results can provide a research idea for the full traversal coverage of agricultural robot in complex farmland environment.
- agricultural robot,
- path planning,
- simulated annealing algorithm,
- A* algorithm,
- path repetition rate

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References

[1]	LAN Y B, CHEN S D. Current status and trends of plant protection UAV and its spraying technology in China[J]. International Journal of Precision Agricultural Aviation, 2018, 1(1): 1-9.
[2]	GONG J L, WANG M X, ZHANG Y F, et al. Flow and sound field analysis of agricultural ultrasonic atomizing nozzle[J]. International Journal of Precision Agricultural Aviation, 2019, 2(2): 32-37.
[3]	胡平志, 杨小柳, 李泽滔. 复杂山地环境下的机器人路径规划[J]. 计算机仿真, 2021, 38(3): 286-291. doi: 10.3969/j.issn.1006-9348.2021.03.059
[4]	王红君, 叶荣, 赵辉, 等. 基于改进的烟花-蚁群算法和B样条曲线的农业机器人路径规划[J]. 科学技术与工程, 2021, 21(7): 2730-2736. doi: 10.3969/j.issn.1671-1815.2021.07.025
[5]	杨保海, 任全会, 李海生. 复杂环境下果园机器人路径规划方法研究[J]. 中国农机化学报, 2021, 42(2): 134-138.
[6]	贺利乐, 刘小罗, 黄天柱, 等. 移动机器人全覆盖路径规划算法研究[J]. 机械设计与制造, 2021(3): 280-284. doi: 10.3969/j.issn.1001-3997.2021.03.064
[7]	杨奇峰, 曲道奎, 徐方. 基于障碍物运动预测的移动机器人路径规划[J]. 计算机工程与设计, 2021, 42(1): 182-188.
[8]	LIU J, YANG J, LIU H, et al. An improved ant colony algorithm for robot path planning[J]. Soft Computing, 2017, 21(19): 5829-5839. doi: 10.1007/s00500-016-2161-7
[9]	JIAO Z, MA K, RONG Y, et al. A path planning method using adaptive polymorphic ant colony algorithm for smart wheelchairs[J]. Journal of Computational Science, 2018, 25: 50-57. doi: 10.1016/j.jocs.2018.02.004
[10]	胡诗宇. 清洁机器人的定位与全覆盖路径规划研究[D]. 南京: 东南大学, 2019.
[11]	宫金良, 王伟, 张彦斐, 等. 基于农田环境的农业机器人群协同作业策略[J]. 农业工程学报, 2021, 37(2): 11-19. doi: 10.11975/j.issn.1002-6819.2021.2.002
[12]	FAZLOLLAHTABAR H, HASSANLI S. Hybrid cost and time path planning for multiple autonomous guided vehicles[J]. Applied Intelligence, 2018, 48(1): 482-498.
[13]	欧福超. 基于图像处理的QR码图像预处理的研究[D]. 济南: 山东大学, 2014.
[14]	QIAN Q W, WU J F, WANG Z. Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm[J]. Personal and Ubiquitous Computing, 2019, 23(3): 393-403.
[15]	王彬溶, 谭代伦, 郑伯川. 基于旅行商问题转化和遗传算法求解汽配件喷涂顺序[J]. 计算机应用, 2021, 41(3): 881-886.
[16]	陶丽华, 马振楠, 史朋涛, 等. 基于TSP问题的动态蚁群遗传算法[J]. 机械设计与制造, 2019(12): 147-149. doi: 10.3969/j.issn.1001-3997.2019.12.037
[17]	蔡延光, 陈厚仁, 戚远航. 混沌烟花算法求解旅行商问题[J]. 计算机科学, 2019, 46(S1): 85-88.
[18]	陈科胜, 鲜思东, 郭鹏. 求解旅行商问题的自适应升温模拟退火算法[J]. 控制理论与应用, 2021, 38(2): 245-254. doi: 10.7641/CTA.2020.00090
[19]	庞峰. 模拟退火算法的原理及算法在优化问题上的应用[D]. 长春: 吉林大学, 2006.
[20]	陈新, 袁宇浩, 饶丹. 一种改进A~*算法在无人船路径规划中的应用[J]. 计算机仿真, 2021, 38(3): 277-281. doi: 10.3969/j.issn.1006-9348.2021.03.057

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