丘陵山区田间道路自主行驶转运车及其视觉导航系统研制
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摘要
自然条件的限制使得丘陵山区农产品和物资的田间转运难以实现高安全性的机械化作业。为此,该文研制了一种在丘陵山区田间道路上自主行驶的转运车及其视觉导航系统。该系统采用RTK-GNSS(real-timekinematic-global navigationsatellitesystem,实时动态-全球卫星导航系统)进行路网信息采集、实时定位和路径规划,利用机器视觉进行田间道路识别并提取路径跟踪线;田间道路非路口区域由机器视觉系统进行导航,路口区域采用RTK-GNSS实时定位进行导航。全局路径规划中对A*算法估价函数进行改进,将路口节点处的道路曲率及道路起伏信息引入代价函数。图像处理中强化道路上的阴影处理和信息融合,实现道路与背景的准确分割;然后将道路区域分块求取形心点,拟合后生成道路的虚拟中线作为局部路径的导航线。路径规划仿真表明,改进的A*算法能融合丘陵山地道路起伏变化的特征,规划的路径更合理。转运车自主行驶测试表明,在直线路径、多曲率复杂路径以及地形起伏路径3种工况下,自主行驶轨迹与实际道路中线的平均偏差分别为0.031、0.069和0.092 m,最大偏差分别为0.133、0.195和0.212 m;转运车沿道路中线自主行驶的平均相对误差分别为5.16%、11.5%和15.3%,满足田间道路转运车自主行驶的安全要求。
In hilly areas, it is difficult to realize mechanized transportation with high safety for agricultural products and materials due to constraints of natural conditions. With the gradually decreasing of rural labor force, farmers in hilly areas urgently need highly automatic field road transfer trolley to reduce the amount of labor required to transport agricultural products and to increase productivity. In this paper, an autonomous driving transfer trolley with visual navigation system for hilly areas were developed and studied. The transfer trolley mainly consisted of drive and brake system, control system, autonomous navigation system, ultrasonic radar obstacle detection system and automatic steering system. The autonomous navigation system included a RTK-GNSS(real-time kinematic-global navigation satellite system) and a machine vision module. The RTK-GNSS functions as road coordinate information collecting, real-time positioning and path planning, the machine vision module functions as field road identifying and path tracking line extracting. To avoid the effect of incorrect positioning resulted from occasional GNSS signal outages due to obstacles such as trees and crops along both sides of the field road, autonomous guidance was implemented by the machine vision module at the non-intersection segments of the road, while it was implemented by the RTK-GNSS at the intersection segments of the road. According to the features of field road with large curvature and fluctuation, in the global path planning, an improved A* algorithm was presented through adjusting the evaluation function by introducing the curvature at intersection nodes and fluctuation information of the road into cost function. In the field road image processing, in order to better distinguish the road area from its surroundings, V component of HSV(hue-saturation-value) color space was used for image segmentation, and S component and V component, after point operating, were fused by weighted method to identify the shadows on the road. Then the shadow regions were combined with the segmented road region. After obtaining the accurate road region in the image, the region was divided into 12 blocks and the centroid points of each block were extracted and smoothed to form a virtual line which was taken as the autonomous navigation line on the road. According to driving speed of the transfer trolley, points on the navigation line were selected as targets for preview tracking control, and a fusion method of front wheel steering angle was used to realize the transition between 2 sequential images. A global path planning simulation test based on actual field road network information was performed to compare the results between the improved A* algorithm and the Dijkstra algorithm. The simulation results showed that: compared with the Dijkstra algorithm, the accumulated altitude change of the path planned by the improved A* algorithm reduced 29.87%, and the total energy consumption of the transfer trolley through the path reduced 29.4% accordingly. Therefore, the improved A* algorithm was more suitable for field roads with large curvature and fluctuation and the corresponding planed path was more reasonable. An actual driving test on a 1.2 m wide field road was carried out. The transfer trolley was set to automatic driving with a constant speed of 2 m/s. To survey the deviation between the autonomous travel trajectory and the actual midline of the road under various conditions in hilly areas, 3 types of field roads, namely straight, complex multi-curvature and fluctuating roads, were selected as test roads. The autonomous driving test showed that: the mean deviations between the actual midline of the road and the automatic travel trajectory on straight roads, multi-curvature complex roads and undulating roads were 0.031, 0.069 and 0.092 m, and the maximum deviations were 0.133, 0.195 and 0.212 m, respectively. Taking the distance from road edge to road centerline as the calculating basis, the average relative errors of the transfer trolley, automatic traveling along the road centerline of these 3 roads, were 5.16%, 11.5% and 15.3%, respectively, the autonomous visual navigation system meeted the safety requirements of autonomous driving transfer trolley on field roads in hilly areas.
In hilly areas, it is difficult to realize mechanized transportation with high safety for agricultural products and materials due to constraints of natural conditions. With the gradually decreasing of rural labor force, farmers in hilly areas urgently need highly automatic field road transfer trolley to reduce the amount of labor required to transport agricultural products and to increase productivity. In this paper, an autonomous driving transfer trolley with visual navigation system for hilly areas were developed and studied. The transfer trolley mainly consisted of drive and brake system, control system, autonomous navigation system, ultrasonic radar obstacle detection system and automatic steering system. The autonomous navigation system included a RTK-GNSS(real-time kinematic-global navigation satellite system) and a machine vision module. The RTK-GNSS functions as road coordinate information collecting, real-time positioning and path planning, the machine vision module functions as field road identifying and path tracking line extracting. To avoid the effect of incorrect positioning resulted from occasional GNSS signal outages due to obstacles such as trees and crops along both sides of the field road, autonomous guidance was implemented by the machine vision module at the non-intersection segments of the road, while it was implemented by the RTK-GNSS at the intersection segments of the road. According to the features of field road with large curvature and fluctuation, in the global path planning, an improved A* algorithm was presented through adjusting the evaluation function by introducing the curvature at intersection nodes and fluctuation information of the road into cost function. In the field road image processing, in order to better distinguish the road area from its surroundings, V component of HSV(hue-saturation-value) color space was used for image segmentation, and S component and V component, after point operating, were fused by weighted method to identify the shadows on the road. Then the shadow regions were combined with the segmented road region. After obtaining the accurate road region in the image, the region was divided into 12 blocks and the centroid points of each block were extracted and smoothed to form a virtual line which was taken as the autonomous navigation line on the road. According to driving speed of the transfer trolley, points on the navigation line were selected as targets for preview tracking control, and a fusion method of front wheel steering angle was used to realize the transition between 2 sequential images. A global path planning simulation test based on actual field road network information was performed to compare the results between the improved A* algorithm and the Dijkstra algorithm. The simulation results showed that: compared with the Dijkstra algorithm, the accumulated altitude change of the path planned by the improved A* algorithm reduced 29.87%, and the total energy consumption of the transfer trolley through the path reduced 29.4% accordingly. Therefore, the improved A* algorithm was more suitable for field roads with large curvature and fluctuation and the corresponding planed path was more reasonable. An actual driving test on a 1.2 m wide field road was carried out. The transfer trolley was set to automatic driving with a constant speed of 2 m/s. To survey the deviation between the autonomous travel trajectory and the actual midline of the road under various conditions in hilly areas, 3 types of field roads, namely straight, complex multi-curvature and fluctuating roads, were selected as test roads. The autonomous driving test showed that: the mean deviations between the actual midline of the road and the automatic travel trajectory on straight roads, multi-curvature complex roads and undulating roads were 0.031, 0.069 and 0.092 m, and the maximum deviations were 0.133, 0.195 and 0.212 m, respectively. Taking the distance from road edge to road centerline as the calculating basis, the average relative errors of the transfer trolley, automatic traveling along the road centerline of these 3 roads, were 5.16%, 11.5% and 15.3%, respectively, the autonomous visual navigation system meeted the safety requirements of autonomous driving transfer trolley on field roads in hilly areas.
引文
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[2]王小娟,李云伍,刘得雄,等.基于机器视觉的丘陵山区田间道路虚拟中线提取方法[J].西南大学学报:自然科学版,2018,40(4):162-169.Wang Xiaojuan,Li Yunwu,Liu Dexiong,et al.A machine vision-based method for detecting virtual midline of field roads in the hilly areas[J].Journal of Southwest University:Natural Science Edition,2018,40(4):162-169.(in Chinese with English abstract)
[3]胡静涛,高雷,白晓平,等.农业机械自动导航技术研究进展[J].农业工程学报,2015,31(10):1-10.Hu Jingtao,Gao Lei,Bai Xiaoping,et al.Review of research on automatic guidance of agricultural vehicles[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(10):1-10.(in Chinese with English abstract)
[4]张铁民,李辉辉,陈大为,等.多源传感器信息融合的农用小车路径跟踪导航系统[J].农业机械学报,2015,46(3):37-42.Zhang Tiemin,Li Huihui,Chen Dawei,et al.Agricultural vehicle path tracking navigation system based on information fusion of multi-source sensor[J].Transactions of Chinese Society for Agricultural Machinery,2015,46(3):37-42.(in Chinese with English abstract)
[5]刁智华,赵明珍,宋寅卯,等.基于机器视觉的玉米精准施药系统作物行识别算法及系统实现[J].农业工程学报,2015,31(7):47-52.Diao Zhihua,Zhao Mingzhen,Song Yinmao,et al.Crop line recognition algorithm and realization in precision pesticide system based on machine vision[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(7):47-52.(in Chinese with English abstract)
[6]刘兆朋,张智刚,罗锡文,等.雷沃ZP9500高地隙喷雾机的GNSS自动导航作业系统设计[J].农业工程学报,2018,34(1):15-21.Liu Zhaopeng,Zhang Zhigang,Luo Xiwen,et al.Design of automatic navigation operation system for Lovol ZP9500high clearance boom sprayer based on GNSS[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(1):15-21.(in Chinese with English abstract)
[7]张美娜,吕晓兰,陶建平,等.农用车辆自动导航控制系统设计与试验[J].农业机械学报,2016,47(7):42-47.Zhang Meina,LüXiaolan,Tao Jianping,et al.Design and experiment of automatic guidance control system in agricultural vehicle[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(7):42-47.(in Chinese with English abstract)
[8]刘军,袁俊,蔡骏宇,等.基于GPS/INS和线控转向的农业机械自动驾驶系统[J].农业工程学报,2016,32(1):46-53.Liu Jun,Yuan Jun,Cai Junyu,et al.Autopilot system of agricultural vehicles based on GPS/INS and steer-by-wire[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(1):46-53.(in Chinese with English abstract)
[9]刘柯楠,吴普特,朱德兰,等.基于GPS的太阳能平移式喷灌机自动导航系统设计与试验[J].农业工程学报,2016,32(16):84-94.Liu Kenan,Wu Pute,Zhu Delan,et al.Design and experiment of autonomous navigation system for solar powered linear move sprinkler machine based on GPS[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(16):89-94.(in Chinese with English abstract)
[10]陈艳,张漫,马文强,等.基于GPS和机器视觉的组合导航定位方法[J].农业工程学报,2011,27(3):126-130.Chen Yan,Zhang Man,Ma Wenqiang,et al.Positioning method of integrated navigation based on GPS and machine vision[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2011,27(3):126-130.(in Chinese with English abstract)
[11]彭顺正,坎杂,李景彬.矮化密植枣园收获作业视觉导航路径提取[J].农业工程学报,2017,33(9):45-52.Peng Shunzheng,Kan Za,Li Jingbin.Extraction of visual navigation directrix for harvesting operation in short-stalked and close-planting jujube orchard[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(9):45-52.(in Chinese with English abstract)
[12]罗锡文,张智刚,赵作喜,等.东方红X-804拖拉机的DGPS自动导航控制系统[J].农业工程学报,2009,25(11):139-145.Luo Xiwen,Zhang Zhigang,Zhao Zuoxi,et al.Design of DGPS navigation control system for Dongfanghong X-804tractor[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2009,25(11):139-145.(in Chinese with English abstract)
[13]杨柳,罗婷婷,程新荣,等.基于Raspberry Pi的拖拉机通用自动驾驶系统[J].农业工程学报,2015,31(21):109-115.Yang Liu,Luo Tingting,Cheng Xinrong,et al.Universal autopilot system of tractor based on Raspberry Pi[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(21):109-115.(in Chinese with English abstract)
[14]张硕,刘进一,杜岳峰,等.基于速度自适应的拖拉机自动导航控制方法[J].农业工程学报,2017,33(23):48-55.Zhang Shuo,Liu Jinyi,Du Yuefeng,et al.Method on automatic navigation control of tractor based on speed adaptation[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(23):48-55.(in Chinese with English abstract)
[15]Kurita H,Iida M,Cho W,et al.Rice autonomous harvesting:Operation framework[J].Journal of Field Robotics,2017,34(6):1084-1099.
[16]Thanpattranon P,Ahamed T,Takigawa T.Navigation of autonomous tractor for orchards and plantations using a laser range finder:Automatic control of trailer position with tractor[J].Biosystems Engineering,2016,147(1):90-103.
[17]Backman J,Oksanen T,Visala A.Navigation system for agricultural machines:Nonlinear model predictive path tracking[J].Computers and Electronics in Agriculture,2012,82(1):32-43.
[18]Ball D,Upcroft B,Wyeth G,et al.Vision-based obstacle detection and navigation for an agricultural robot[J].Journal of Field Robotics,2016,33(8):1107-1130.
[19]Garcia Santillan I,Guerrero J M,Montalvo M,et al.Curved and straight crop row detection by accumulation of green pixels from images in maize fields[J].Precision Agric,2018,19(1):18-41.
[20]Radcliffe J,Cox J,Bulanon D M.Machine vision for orchard navigation[J].Computers in Industry,2018,98(1):165-171.
[21]Lyu H,Park C,Han D.Orchard free space and center line estimation using naive Bayesian classifier for unmanned ground self-driving vehicle[J].Symmetry,2018,10(9):355.
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