| 摘要: |
| 为提高果园割草机的工作效率，降低作业成本，提出一种改进粒子群优化算法(Improved particle swarm optimization，IPSO)以解决矩形果园环境下的割草机作业路径规划问题。对苹果园割草场景下的作业路径特点进行分析，将路径规划问题转化为割草机作业行的调度排优问题，考虑多种转弯策略，以总转弯距离最小为优化目标，采用粒子群优化算法(Particle swarm optimization，PSO)求解最佳的作业行序列。为增强粒子群的寻优能力，使用随搜索进程非线性动态变化的算法参数及粒子扰动策略对PSO算法进行改进，通过仿真试验及实地试验进行验证。结果表明:1)6种不同作业行数下，与PSO算法相比，IPSO算法收敛速度减慢，算法耗时平均增加约1.0~2.5 s，但均能找到总转弯距离更少的作业路径，总转弯距离减少率为7.52%~32.72%；2)不同割草机参数(作业幅宽、最小转弯半径)下，与PSO算法相比，IPSO算法均能找到总转弯距离更少的作业路径；3)在果园环境与割草机机型确定的实际作业情况下，与传统方法和PSO算法相比，IPSO算法均能找到油耗更小的作业路径，节省油耗分别为 22.51%和1.57%。 |
| 关键词: 割草机 路径规划 粒子群优化算法 非线性变化 粒子扰动 |
| DOI：10.11841/j.issn.1007-4333.2023.11.16 |
| 投稿时间：2023-03-22 |
| 基金项目:国家现代农业产业技术体系建设专项(CARS-27)；河北省教育厅在读研究生创新能力培养项目(CXZZBS2023080) |
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| Orchard lawn mower operation path planning based on improved particle swarm optimization algorithm |
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XIE Jinyan,LIU Lixing,YANG Xin,WANG Xu,WANG Xiaosa,CHEN nuo
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| (College of Electrical and Mechanical Engineering, Hebei Agricultural University, Baoding 071000, China) |
| Abstract: |
| To improve the efficiency of the orchard lawn mower and reduce the operation cost, an improved particle swarm optimization(IPSO)algorithm for lawn mower operation path planning in rectangular orchard environment was proposed in this study. After analyzing the characteristics of the mowing operation path in the apple orchard, transforming the path planning problem into the scheduling problem of the lawn mower operation rows and considering a variety of turning strategies, the particle swarm optimization(PSO)algorithm was used to solve the optimal sequence of operation rows while the optimization goal was to make the total turning distance minimum. To enhance the search ability of particle swarm, algorithm parameters, which change with the search process nonlinearly and dynamically and the disturbance strategy, were proposed to improve the PSO algorithm. The IPSO algorithm was verified by simulation experiments and field experiments. The results showed that: 1)Under 6 different row numbers, compared with the PSO algorithm, the convergence speed of the IPSO algorithm slowed down and the algorithm time increased by an average of about 1. 0 to 2. 5 s. The IPSO algorithm always found the mowing path with less total turning distance, and with a reduction rate from 7. 52% to 32. 72%; 2)Under different mower parameters(working width, minimum turning radius), compared with the PSO algorithm, the IPSO algorithm could find the working path with less total turning distance; 3)In the actual operation situation, compared with the traditional method and PSO algorithm, the IPSO algorithm could find the operation path with less fuel consumption, and the fuel consumption was saved by 22. 51% and 1. 57%, respectively. |
| Key words: lawn mower path planning particle swarm optimization algorithm nonlinear variation disturbing particles |
