Kazunobu Ishii

Field Automation Using Robot Tractor

The objective of the study was to develop a field robot in agricultural operation environment. The navigation sensor consisted of an RTK-GPS and an inertial measurement unit (IMU). The sensor fusion algorithm was capable of identifying FOG bias and compensating location error in real-time for providing sufficient navigation information in support of accurate robot guidance in the field. The field tests of a field robot have been conducted in Sapporo, Japan. Tillage, planting, cultivating and splaying on soybean field has been conducted. In addition, the robot itself could transfer between a shed and a field to be operated. The accuracy of the vehicle was better than skilled farmer’s operation. The adopted speed of the vehicle was conventional human operation speeds. The r.m.s. lateral error of the guided vehicle was less than 5 cm. Even crop row was slightly curved, the autonomous vehicle could travel without running over the crops.

Development of Robot Tractor Based on RTK-GPS and Gyroscope

This study developed a field robot for an agricultural operating environment. The navigation sensor consisted of an RTK-GPS, a fiber optic gyroscope (FOG), and an inertial measurement unit (IMU). A sensor fusion algorithm was used to identify FOG bias and compensate for location error in real-time, thus providing sufficient navigation information to support accurate robot guidance in the field. The guidance system could guide the agricultural robot automatically to follow either straight or curve paths including crop rows at a speed of 2.5 m/s. This RMS position error of the desired pathway in the field was less than 3 cm. The results indicated that the navigation system was capable of guiding an agricultural robot accurately and robustly under normal agricultural operations.

Field information system using an agricultural helicopter towards precision farming

The objective of this study is to develop the system that can generate a map regarding field information such as crop status and land topographical feature obtained by an imaging sensor and laser range finder mounted on an unmanned helicopter. To generate a precise map, accurate measurement of helicopters position and posture are essential, because the geometric distortions occur in the image taken from the helicopter due to variations of the sensing position and posture. In the research, an RTK-GPS was adopted as a positioning sensor and the helicopters posture was obtained by an INS. Furthermore, the encoders can measure the imaging sensors motion. The transformation method to generate a map using these parameters was developed. And a topographic map of a field was generated by adopting laser range finder as another sensor.

The Development of Remote Sensing System Using Unmanned Helicopter

The objective of this study is to develop the system that can generate a map regarding crop statusobtained by the imaging sensor mounted on an unmanned helicopter. As for the unmannedhelicopter used in this research, an RTK-GPS was adopted as positioning sensor, and an inertialsensor that provides posture (roll and pitch angles) is installed in the helicopter. Moreover, ageomagnetic direction sensor (GDS) that outputs an absolute direction is also attached in thehelicopter. When obtaining the image by the image sensor on the helicopter, some distortionscaused by change of helicopter’s posture arise in the image. In order to remove this distortion,geometric correction by converting from image coordinate to global coordinate is badly needed.But there are some errors in posture data, particularly large GDS error was caused by warp ofgeomagnetism surrounding the helicopter. Therefore the method of correction of GDS errors wasdeveloped in the study. As the result, it was possible to generate a map including 41 cm error usingthe image taken by the helicopter.

Automatic Navigation of the Agricultural Vehicle by the Geomagnetic Direction Sensor and Gyroscope

The objective of this research is to develop an automatic guidance system composed of relatively low cost sensors. As a navigation sensor, we used a geomagnetic direction sensor (GDS) and a gyroscope. To increase the accuracy of these sensors outputs and navigation performance, these sensor output were integrated based on sensor fusion technique. In this paper, the precise estimation method of the vehicle orientation was proposed. First, the noise of the GDS and inclinometer were eliminated by the adaptive line enhancer(ALE). As the result of inclination correction of the GDS by ALE, the corrected GDS was consistent with the actual orientation. Second, the drift error of the gyroscope was estimated by least square method(LSM). Third, these sensors output were integrated to obtain the accurate vehicle orientation. Finally, to evaluate the accuracy of the proposed estimation method of orientation, the automatic navigation test was carried out in the actually used field. As a result of the automatic navigation test, it was confirmed that proposed estimation method had the ability to navigate the mobile vehicle with high accuracy.

Development of the Agricultural Autonomous Tractor with an RTK-GPS and a Fog

ABSTRACT The autonomous tractor system engaged in all type of operations at fields was developed in the paper. The developed system adopted an RTK-GPS and a Fiber Optical Gyroscope (FOG) as navigation sensors. The sensor-fusion algorithm based on Least Squares Method for estimating FOG bias in each step for detecting an absolute vehicle heading was developed. An Autonomous tractor has to have a mission planner including a path planner, because the tractor uses some types of implement under various settings of PTO, transmission, and engine speed. The navigation maps consisted of a desired path and commands including PTO status, transmission, and other functions were generated by a GIS software for each farm operation. The developed system could autonomously perform a rotary tillage with 6 cm error from the scheduled path at 1.5 m/s by applying the navigation map.

Development of a Low-cost IMU by Using Sensor Fusion for Attitude Angle Estimation

Abstract The objective of this research was to explore the use of data information of a low-cost IMU to provide an attitude angle with acceptable accuracy for agricultural robot. This work was an attempt to create attitude angle estimation system via sensor fusion method based on a triple gyroscope and a tri-axis accelerometer in this low-cost IMU. The used algorithm processed and integrated the data from the gyroscope and the accelerometer using a mean filter and a Kalman filter. Under this algorithm, the experiment data showed that the estimation precision was improved effectively. It can solve noise jamming, and be especially suitable for the robot which is sensitive to the payload and cost effective.

Development of a Robot Combine Harvester for Wheat and Paddy Harvesting

Abstract Currently, the number of farmers in Japan is decreasing at an especially high speed. In addition, the average age of the farmers available is increasing. In this trend, it is entirely possible that several decades later, the self-sufficiency rate of agricultural product in Japan will drop sharply. To solve this problem, one possible solution should be to employ agricultural robot in actual use, including robot tractor, robot combine harvester and so on. Recognizing the importance and urgency of developing such agricultural robots, this study proposed and developed a robot combine harvester. This robot relies on an AGI GPS receiver and IMU for position and posture data. It is controlled via CAN BUS. In this study, its accuracy was tested, and it shows that this combine harvester can be applied in Honshu and Hokkaido area of Japan with this accuracy.

Crop Status Sensing Based on Machine Vision for Precision Farming

Abstract Sensors are an essential part of intelligent agricultural machinery. Machine vision, in particular, can supply information about current crop status, including maturity and weed infestations. The information gathered through machine vision and other sensors such as GPS can be used to create field management schedules for chemical application, cultivation and harvest. The purpose of the study is to develop an intelligent machine vision system for an agricultural mobile robot A mUlti-spectral imaging system was developed to remotely obtain crop status on a field. The developed multi-spectral imaging system (MSIS) consists of an imaging sensor, an ilumination sensor, a differential GPS, and a portable computer. The imaging sensor was a custom-developed 3-CCD camera, which contains t~lfee separate optical paths and CCD image plane. Special optical filters were installed over the sensors providing tllfee video channels of Green (G), Red (R), and near infrared (NIR). The field experiment of MSIS was conducted using cornfield. The r 2 for estimation of the crop height showed 0.73. And, the r 2 for estimation of the SPAD value showed 0.92 by using both reflectance and leaf area infonnation. As a result, the developed vision system enables a robot to recognize the crop status and efficiently conduct field operation through the timely information. The outputs include crop stress maps with nitrogen deficiency indexes on the field which is available on building a database for precise field management.

Development of Robot Tractor Using the Low-Cost GPS/INS System

In this research, low cost navigation sensor unit composed of three vibratory gyroscopes and two inclinometers and the GPS for the agricultural vehicle was developed. The sensor can provide the position corrected by inclination, heading angle and roll/pitch inclinations. The microcomputer (H8S2612) was installed to measure the sensors data and calculate the corrected position, corrected heading and corrected inclination by itself without using laptop computer and high resolution A/D converter. To measure the accuracy of developed sensor unit, the field test was conducted. The developed sensor unit could estimate the heading angle with 1.59° and roll angle with 0.41° and pitch angle with 0.65°. This accuracy was equivalent to the accuracy of RTK-GPS and IMU system using high resolution A/D converter and laptop computer.