农业机器人车辆人机协作控制的动态功能分配研究*

doi:10.12398/j.issn.2096-7217.2020.01.004

智能化农业装备学报（中英文） ›› 2020, Vol. 1 ›› Issue (1): 24-31.DOI: 10.12398/j.issn.2096-7217.2020.01.004

农业机器人车辆人机协作控制的动态功能分配研究^*

毛婷婷, 董淑娴, 薛金林

南京农业大学工学院,南京市,210031

收稿日期:2019-11-19 出版日期:2020-08-15 发布日期:2021-11-30
通讯作者: 薛金林,男,1974年生,博士,教授;研究方向为农业车辆测控技术和智能化。E-mail: xuejinlin@ njau.edu.cn
作者简介:毛婷婷,女,1994年生,硕士研究生;研究方向为农业车辆遥操作系统设计。E-mail:mttnj1024@163.com
基金资助:
江苏省自然科学基金资助(BK20151436);“江苏高校青蓝工程”

Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation^*

Tingting Mao, Shuxian Dong, Jinlin Xue

College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China

Received:2019-11-19 Online:2020-08-15 Published:2021-11-30
Contact: Jinlin Xue, Doctor,professor, research direction: agricultural vehicle measurement and intelligent control. E-mail: xuejinlin@njau.edu.cn
About author:Tingting Mao, Postgraduate, research direction: agricultural vehicle teleoperation system design. E-mail: mttnj1024@163.com
Supported by:
Jiangsu Provincial Natural Science Foundation of China (BK20151436); Jiangsu University Qinglan Project

摘要/Abstract

摘要： 为了实现遥操作农业拖拉机系统中操作员与自动化系统相互协作,共同完成作业任务,需要将功能在人机之间进行合理分配。本文基于对BP神经网络算法、遗传算法和自适应遗传算法三种智能算法对动态功能分配进行研究,操作员的状态、操作员工作量和任务的需求作为动态功能触发机制。首先,利用遗传算法和自适应遗传算法分别对BP神经网络优化,将操作员的状态、操作员工作量和任务的需求作为网络的输入量,输出量是自动化等级,建立了3个网络训练模型,进而得到人机功能分配方案。基于自适应遗传算法BP神经网络模型训练次数远小于基于遗传BP神经网络训练次数,预测准确率也高于基于遗传BP神经网络的预测准侧率。测试结果表明,相比于传统的BP神经网络、遗传BP神经网络,基于自适应遗传BP神经网络更优。

关键词: 遥操作, 农业机器人, 人机功能分配, 遗传算法, 自适应遗传算法, BP神经网络

Abstract: It is necessary to distribute functions reasonably between a human operator and an automation system in a teleoperated agricultural robotic tractor to accomplish a task cooperatively. This paper proposes a strategy of dynamic function allocation on the basis of a BP neural network, genetic algorithm and adaptive genetic algorithm. Here, the operator's state, workload, and task demand are chosen as trigger mechanism of dynamic function allocation. Then, a traditional BP neural network, genetic algorithm based BP neural network, and adaptive genetic algorithm based BP neural network are established by taking the operator's state, workload, and task demand as inputs of the network and automation level as output. The three network are compared to obtain more effective dynamic function allocation. Simulation tests show that the adaptive genetic algorithm based BP neural network has minimum training time and has highest prediction accuracy.

Key words: teleoperation, agricultural robot, function allocation, man-machine cooperation, genetic algorithm, adaptive genetic algorithm, BP neural network

毛婷婷, 董淑娴, 薛金林. 农业机器人车辆人机协作控制的动态功能分配研究^*[J]. 智能化农业装备学报（中英文）, 2020, 1(1): 24-31.

Tingting Mao, Shuxian Dong, Jinlin Xue. Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation^*[J]. Journal of Intelligent Agricultural Mechanization, 2020, 1(1): 24-31.

参考文献

[1] 庄贤雯. 基于细胞注射机器人的遥操作控制系统的研究与设计[D]. 沈阳: 东北大学, 2012.
Zhuang Xianwen. Research and design of teleoperation control system based on cell injection robot [D]. Shenyang: Northeastern University, 2012.
[2] Wu Weiren, Zhou Jianliang, Wang Baofeng, et al. Key technologies in remote operation of chang'e-3 Yutu patrol [J]. Chinese Science: Information Science, 2014, 44(4): 425-440.
[3] Zhang J, Li W, Yu J, et al. Development of a virtual platform for telepresence control of an underwater manipulator mounted on a submersible vehicle [J]. IEEE Transactions on Industrial Electronics, 2017, 64(2): 1716-1727.
[4] Zuo Guoyu, Yu Shuangyue, Gong Daoxiong. Research on the operator attitude calculation method of teleoperation nursing robot system [J]. Journal of automation, 2016, 42(12): 1839-1848.
[5] Khudher D, Powell R, Abbod M. Operational space control in hexapod robot for humanitarian demining applications[C]. Nagoya: International Conference on Control, Automation and Robotics. IEEE, 2017.
[6] Xu Lijia, Ran Chunsen, Wang Wenjuan, et al. Development of small wireless remote control and real-time dispensing sprayer [J]. Journal of Agricultural Engineering, 2012, 28(10): 13-19.
[7] Wang Yuanjie, Liu Yongcheng, Yang Fuzheng, et al. Development and test of greenhouse micro remote control electric tractor [J]. Journal of Agricultural Engineering, 2012, 28(22): 23-29.
[8] Malone T B, Heasly C C. Function Allocation: Policy, practice, procedures & process[J]. Naval Engineers Journal, 2010, 115(2): 49-62.
[9] Johnson R, Lee M, Goldberg D. Adaptive level of autonomy for UAV supervisory control, Final Report for March 2005 to March 2007[R]. Air Force Research Laboratory, Wright-Patterson AFB, OH45433, 2005-09-28.
[10] Lagu A V, Landry S J, Yoo H S. Adaptive function allocation stabilization and a comparison of trigger types and adaptation strategies[J]. International Journal of Industrial Ergonomics, 2013, 43(5): 439-449.
[11] Tang Zhili, Zhang an, Cao Lu, et al. Functional allocation methods of complex human-computer intelligent system [J]. Ergonomics, 2010, 16(1): 68-71.
[12] Dearden A, Harrison M, Wright P. Allocation of function: Scenarios, context and the economics of effort [M]. Academic Press, Inc. 2000, 30(3): 286-297.
[13] Zhou Qianxiang, Jiang Shizhong. Research on human-machine function allocation method of manned spacecraft system [J]. System Engineering and Electronic Technology, 2000, 22(8): 30-33.
[14] Zhou Qianxiang, Zhou Shihua. A model for human-machine function allocation of manned spacecraft [J]. Ergonomics, 2003, 9(2): 3-6.
[15] Zhang Wei, Li Daochun, Song Bifeng. Simulation of dynamic allocation of human and aircraft functions in combat UAV system [J]. Ergonomics, 2005, 11(1): 5-7.
[16] Wang Kuotan, Zhang Guozhong, Shen Lincheng, et al. Research on dynamic function allocation of UAVs under the supervision and control of multiple UAVs [J]. Computer Engineering and Application, 2009, 45(30): 245-248.
[17] Zhang An, Tang Zhili, Zhang Chao. Research on human-machine function allocation method based on Uncertain Linguistic Multi-attribute decision making [J]. Chinese Journal of Aeronautics, 2011, 24(6): 816-822.
[18] Yang Shaozeng. Operator functional state prediction and regulation based on multi-source physiological data and fuzzy modeling method [D]. Shanghai: East China University of Science and Technology, 2014.
[19] Catherine C, Colin C, Catherine M, et al. Investigating methods of dynamic function allocation for naval command and control [C]. An International Conference on People in Control (Human Interfaces in Control Rooms, Cockpits and Command Centres. London: IEE, 1999: 388-393.
[20] Parasuraman R, Sheridan T B, Wickens C D. A model for types and levels of human interaction with automation [J]. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 2000, 30(3): 286-297.
[21] Liu Houlin, Wu Xianfang, Wang Yong, et al. Power prediction of centrifugal pump shut-down point based on BP neural network [J]. Journal of Agricultural Engineering, 2012, 28(11): 45-49.
[22] Meng Zhaoping, Tian Yongdong, Lei Min. BP neural network model and application of coal seam gas content prediction [J]. Journal of China University of Mining and Technology, 2008, 37(4): 456-461.
[23] Li Ning, Zhang Qi, Yang Fuxing, et al. Adaptive genetic neural network algorithm for soil moisture prediction [J]. Computer Engineering and Application, 2018(1): 54-59.
[24] Wang Xiaochuan, Shi Feng, Yu Lei, et al. 43 case studies of MATLAB neural network [M]. Beijing: Beijing University of Aeronautics and Astronautics Press, 2013.
[25] 李海芸, 曹亚磊, 董楸煌, 等. 农业机器人实验平台构建与探索[J]. 中国农机化学报, 2019, 40(1): 210-214.
Li Haiyun, Cao Yalei, Dong Qiuhuang, et al. Construction and exploration of experimental platform for agricultural robot [J]. Journal of Chinese Agricultural Mechanization, 2019, 40(1): 210-214.
[26] 王宝梁, 索明何, 刘大诚. 基于开放式结构的多功能农业机器人设计[J]. 中国农机化学报, 2019, 40(3): 179-184.
Wang Baoliang, Suo Minghe, Liu Dacheng. Design of a multifunctional agricultural robot [J]. Journal of Chinese Agricultural Mechanization, 2019, 40(3): 179-184.
[27] 闫全涛, 李丽霞, 邱权, 等. 小型移动式农业机器人研究现状及发展趋势[J]. 中国农机化学报, 2019, 40(5): 178-186.
Yan Quantao, Li Lixia, Qiu Quan, et al. Research status and development trends of small-mobile agricultural robots [J]. Journal of Chinese Agricultural Mechanization, 2019, 40(5): 178-186.
[28] 王志伟, 许江淳, 李玉惠, 等. 基于PSO-EACO的农业机器人路径规划仿真研究[J]. 中国农机化学报, 2018, 39(10): 103-106.
Wang Zhiwei, Xu Jiangchun, Li Yuhui, et al. Simulation research on agricultural robot path planning based on PSO-EACO [J]. Journal of Chinese Agricultural Mechanization, 2018, 39(10): 103-106.
[29] 王伟, 杜向阳. 基于捕食搜索策略的遗传算法在矩形件优化排样中的研究[J]. 中国农机化学报, 2019, 40(2): 157-162.
Wang Wei, Du Xiangyang. Genetic algorithm based on predatory search strategy for rectangular optimal layout [J]. Journal of Chinese Agricultural Mechanization, 2019, 40(2): 157-162.
[30] 阮承治, 赵德安, 陈旭, 等. 双指型农业机器人抓取球形果蔬的控制器设计[J]. 中国农机化学报, 2019, 40(11): 169-175.
Ruan Chengzhi, Zhao De'an, Chen Xu, et al. Controller design for realizing double-finger agricultural robot to grasp spherical fruits and vegetables [J]. Journal of Chinese Agricultural Mechanization, 2019, 40(11): 169-175.
[31] 安小宇, 鲁奎豪, 崔光照. 基于改进樽海鞘优化BP神经网络的土壤墒情预测[J]. 中国农机化学报, 2019, 40(11): 124-130.
An Xiaoyu, Lu Kuihao, Cui Guangzhao. Prediction of soil moisture based on BP neural network optimized by adaptive salp swarm algorithm [J]. Journal of Chinese Agricultural Mechanization, 2019, 40(11): 124-130.
[32] 张开兴, 吕高龙, 贾浩, 等. 基于图像处理和BP神经网络的玉米叶部病害识别[J]. 中国农机化学报, 2019, 40(8): 122-126.
Zhang kaixing, Lv Gaolong, Jia Hao, et al. Identification of corn leaf disease based on image processing and BP neural network [J]. Journal of Chinese Agricultural Mechanization, 2019, 40(8): 122-126.

农业机器人车辆人机协作控制的动态功能分配研究^*

Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation^*

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	王星辰, 潘伟, 周立春, 侯英勇, Petr Bartos, 肖茂华. 基于CS-BP神经网络的巡检机器人位移补偿方法研究[J]. 智能化农业装备学报（中英文）, 2022, 3(2): 53-63.
[2]	王鹏军, 陈永生, 孙冬霞, 吴爱兵, 郝延杰, 陈明江. 基于BP神经网络算法的固体有机肥撒施装置控制系统设计^*[J]. 智能化农业装备学报（中英文）, 2021, 2(1): 13-19.
[3]	赵静娟, 杨艳萍, 郑怀国, 董瑜. 全球农业机器人研发趋势预测^*[J]. 智能化农业装备学报（中英文）, 2021, 2(1): 28-35.

农业机器人车辆人机协作控制的动态功能分配研究*

Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation*

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

农业机器人车辆人机协作控制的动态功能分配研究^*

Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation^*