| 履带式收割机全田块路径跟踪算法设计与试验 |
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| 引用本文： | 章少岑,魏新华,邓屹,臧崇全,吉鑫,汪岸哲.履带式收割机全田块路径跟踪算法设计与试验[J].农业工程学报,2023,39(20):36-45. |
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| 作者姓名： | 章少岑 魏新华 邓屹 臧崇全 吉鑫 汪岸哲 |
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| 作者单位： | 江苏大学农业工程学院，镇江 212013;江苏大学农业工程学院，镇江 212013;现代农业装备与技术教育部重点实验室，镇江 212013;常州江苏大学工程技术研究院，常州，213164 |
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| 基金项目： | 国家重点研发计划项目（2022ZD0115804）；上海市科技兴农项目（沪农科推字(2019)第4-3号）；江苏省科技项目（BE2020327） |
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| 摘 要： | 针对中小型传统收割机无人化作业需求，该研究设计了一套后装式全田块自动驾驶系统。以沃得锐龙单边制动转向型履带式收割机为平台，搭建手自兼容自动驾驶系统，并进行系统特性辨识试验，明确其测控性能限制。针对测控性能限制设计一种PD-Fuzzy-BangBang组合路径跟踪算法，并进行样机集成与试验。水泥地面直线行驶试验表明，组合算法相较单一PD算法的上线距离缩短57.3%，稳态标准差缩小81.3%。全田块模拟试验证明，组合算法在理想条件下的路径跟踪最大偏差为6.00 cm，标准差为2.42 cm，样机具备全田块自动驾驶功能。实际水田收割作业试验证明，样机在车速0.7 m/s条件下，上线过程的路径跟踪最大偏差为12.00 cm，标准差为 6.18 cm，全田块不漏割，割幅利用率大于80%，满足田间作业需求。
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| 关 键 词： | 模糊控制 自动驾驶 收割机 路径跟踪 全田块 |
| 收稿时间： | 2023/3/23 0:00:00 |
| 修稿时间： | 2023/9/26 0:00:00 |
Design and experiments of the whole field path tracking algorithm for a track-based harvester |
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| ZHANG Shaocen,WEI Xinhu,DENG Yi,ZANG Chongquan,JI Xin,WANG Anzhe.Design and experiments of the whole field path tracking algorithm for a track-based harvester[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(20):36-45. |
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| Authors: | ZHANG Shaocen WEI Xinhu DENG Yi ZANG Chongquan JI Xin WANG Anzhe |
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| Institution: | School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;Key Laboratory of Modern Agriculture and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China;Changzhou Engineering and Technology Institute of Jiangsu University, Changzhou, 213164 |
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| Abstract: | Intelligent and unmanned agricultural machinery can be very necessary to develop in modern agriculture, due to the shortage of labor force against the urbanization in recent years. It is also in high demand to enhance the utilization of land and the efficiency of the machinery. Among them, autonomous driving can be expected to serve as the key technology for unmanned agricultural machinery. In this study, a set of additional auto-driving systems was established to fully meet the requirement of intelligent improvement on traditional harvesters in the full-field blocks. Firstly, a hand-compatible electric control device was designed for the simple switch between manned and unmanned operations in current agricultural production, according to the control and power system of the traditional harvesters. Secondly, a series of experiments were conducted to test the measurement and control system, as well as the steering characteristics of the single-sided brake steering harvester. The operation performance of the controller was verified using the response of the actuator, together with the relationship between steering valve opening, HST speed, and steering curvature. PD-fuzzy-BangBang joint control was then proposed to improve the accuracy of the real embedded control system, according to the pedrail steering, measurement, and control system. Finally, a prototype was developed for the automatic driving and operation on the cement surface in the rice field. The full-block automatic driving performance was achieved in the joint control system of the prototype. The cement surface online experiment showed that the over-adjustment oscillation of the system was effectively reduced for the steady-state accuracy on the line. Specifically, the online distance of the combined algorithm was shortened by 57.3%, and the steady state standard deviation was reduced by 81.3%, compared with the single PD. The cement ground experiment showed that the prototype better performed the full-block automatic driving under ideal conditions on straight paths, with a maximum deviation of 6.00 cm and a standard deviation of 2.42 cm. The accuracy of the prototype fully met the requirements during actual operation in the rice field, particularly with the over 80% cutting width and the speed of 0.7 m/s. Therefore, full-field unmanned harvesting was realized with the addition of the self-adaptive operation function. The finding can provide technical support and equipment solutions for the construction of unmanned farms. |
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| Keywords: | fuzzy algorithm automatic navigation harvester path tracking full field blocks |
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