Adaptive-coefficient Kalman Filter Based Combined Positioning Algorithm for Agricultural Mobile Robots

QIU Quan, HU Qinghan, FAN Zhengqiang, SUN Na, ZHANG Xihai

Abstract

GNSS-based positioning and navigation has been widely used for agricultural robots in open unmanned farms. However, for the applications of semi-structured and semi-open agricultural scenarios, there may be temporary loss of GNSS received signals caused by occlusion of canopies in some areas, which will affect the positioning and navigation accuracy of robots and even harm crops or farmers. To solve this problem, a combined positioning method of GNSS and INS under the occlusion environment of agriculture was studied. The main work consisted of three parts: a mobile agricultural robot system was build up for the experiments of multi-sensor-based positioning and navigation, which consisted of hardware (track-layer mobile platform, GNSS receivers and INS, etc.) and software (ROS, remote control interface, etc.);an adaptive-coefficient Kalman filter based combined positioning algorithm was proposed. When the GNSS signal was unstable or denied, the new algorithm can switch to INS positioning adaptively based on Kalman filter, which carried out the optimal estimation for the robots’ location and gesture;experiments of the proposed combined positioning algorithm were conducted under practical scenes of agriculture, in which four different positioning methods (GNSS only, INS only, Kalman filter based combined positioning, and adaptive-coefficient Kalman filter based combined positioning) were compared to validate the effectiveness of the algorithm. Field experiments showed that in the process of combined positioning, compared with GNSS positioning, INS positioning and conventional Kalman filter fusion positioning, the positioning accuracy of adaptive-coefficient Kalman filter in the 30m×6m high shaded area of 100m×20m experimental area was improved by 62.1%,48.5% and 47.7%, respectively.


Keywords: agricultural mobile robot, combined positioning, global navigation satellite system, inertial navigation system, adaptive-coefficient Kalman filter


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JIA W К, TIAN Y Y, DUAN II C, el al. Autonomous navigation control based on improved adaptive filtering for agricultural robot[ J]. International Journal of Advanced Robotic Systems, 2020, 17(4) ; 1 - 12.

LIU Y, MA X Y, SHU L, et al. From Industry 4.0 to Agriculture 4.0; current status, enabling technologies, and research challenges [J ]. IEEE Transactions on Industrial Informatics, 2021 , 17(6) ; 4322 -4334.

ALBIERO D, DE PAULO R L, JUNIOR J С F, et al. Agriculture 4.0; a terminological introduction [ J . Revista Ciencia Agronomica, 2020, 51 (Special Agriculture 4. 0) ; e20207737.

LI Daoliang, LI Zhen. System analysis and development prospect of unmanned farming J . Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(7) ; 1 - 12. (in Chinese)

VASCONEZ J P, KANTOR G A, CHEEIN F A A. Human-robot interaction in agriculture; a survey and current challenges [J]. Biosystems Engineering, 2019, 179; 35 -48.

XIE В В, LIU J Z, HE M, et al. Research progress on autonomous navigation technology of agricultural robot[C] / 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), IEEE, 2021 : 891 -898.

LI J, YIN J L, DENG L. A robot vision navigation method using deep learning in edge computing environment [J ]. EURASIP Journal on Advances in Signal Processing, 2021 , 22 ;2 -21.

JIN Y C, LIU J Z, XU Z J, et al. Development status and trend of agricultural robot technology [ J ]. International Journal of Agricultural and Biological Engineering, 2021 , 14(4); 1 -19.

YU Fenghua, ZHOU Chuanqi, YANG Xin, et al. Design and experiment of tomato picking robot in solar greenhouse [ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53( 1 ) ; 41 -49. (in Chinese)

CHEN X Y, CHAUDHARY K, TANAKA Y, et al. Reasoning-based vision recognition for agricultural humanoid robot toward tomato harvesting [ С ] // 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany: IEEE, 2015 : 6487 -6494.

ZHOU H, HU L, LUO X W, et al. Design and test of laser-controlled paddy field levelling-beaterf J ]. International Journal of Agricultural and Biological Engineering, 2020, 13(1): 57 -65.

HAN Changjie, XIAO Liqiang, XU Yang, et al. Design and experiment of the automatic transplanter for chili plug seedlings [J]. Transactions of the CSAE, 2021, 37(13): 20 -29. (in Chinese)

MCCOOL С, BEATTIE J, FIRN J, et al. Efficacy of mechanical weeding tools; a study into alternative weed management strategies enabled by robotics[J]. IEEE Robotics and Automation Letters, 2018, 3(2) ; 1184 - 1190.

ZHAI Changyuan, FU Hao, ZHENG Kang, et al. Establishment and experimental verification of deep learning model for on¬line recognition of field cabbage[ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(4) : 293 - 303. (in Chinese)

WEN Y S, ZHANG L A, HUANG X M, et al. Design of and experiment with seedling selection system for automatic transplanter for vegetable plug seedlings[ J ]. Agronomy-Basel, 2021 , 1 1 ( 10) :2031.

HU Dandan, YIN Huan. Path recognition of corn harvesting robot based on machine vision J ]. Journal of Agricultural Mechanization Research, 2017, 39( 12) : 190 - 194. (in Chinese)

JONES M H, BELL J, DREDGE D, et al. Design and testing of a heavy-duty platform for autonomous navigation in kiwifruit orchards [J ]. Biosystems Engineering, 2019, 187: 129 -146.

HOU Jialin, PU Wenyang, LI Tianhua, et al. Development of dual-lidar navigation system for greenhouse transportation robot [ J . Transactions of the CSAE, 2020, 36 ( 14) ; 80 - 88. (in Chinese)

KANG H, ZHOU H, CHEN C. Visual perception and modeling for autonomous apple harvesting [ J]. IEEE Access, 2020, 8: 62151 -62163.

MOELLER R, DEEMYAD T, SEBASTIAN A. Autonomous navigation of an agricultural robot using RTK GPS and Pixhawk [C] //2020 Intermountain Engineering, Technology and Computing (IETC), IEEE, 2020: 1 -6.

LUO Xiwen, ZHANG Zhigang, ZHAO Zuoxi, et al. Design of DGPS navigation control system for Dongfanghong X -804 tractor[ J ]. Transactions of the CSAE, 2009, 25(11) : 139 -145. (in Chinese)


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