Noboru Noguchi

Field Automation Using Robot Tractor

The objective of the study was to develop a field robot in agricultural operation environment. nThe navigation sensor consisted of an RTK-GPS and an inertial measurement unit (IMU). The nsensor fusion algorithm was capable of identifying FOG bias and compensating location error in nreal-time for providing sufficient navigation information in support of accurate robot guidance in nthe field. The field tests of a field robot have been conducted in Sapporo, Japan. Tillage, nplanting, cultivating and splaying on soybean field has been conducted. In addition, the robot nitself could transfer between a shed and a field to be operated. The accuracy of the vehicle was nbetter than skilled farmer’s operation. The adopted speed of the vehicle was conventional human noperation speeds. The r.m.s. lateral error of the guided vehicle was less than 5 cm. Even crop row nwas 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 nnavigation sensor consisted of an RTK-GPS, a fiber optic gyroscope (FOG), and an inertial nmeasurement unit (IMU). A sensor fusion algorithm was used to identify FOG bias and ncompensate for location error in real-time, thus providing sufficient navigation information to nsupport accurate robot guidance in the field. The guidance system could guide the agricultural nrobot automatically to follow either straight or curve paths including crop rows at a speed of 2.5 nm/s. This RMS position error of the desired pathway in the field was less than 3 cm. The results nindicated that the navigation system was capable of guiding an agricultural robot accurately and nrobustly under normal agricultural operations.

Turning Function for Robot Tractor Based on Spline Function

A challenge for autonomous field robotic systems for agriculture is preserving the ngeneral-purpose capabilities of the machine for various types of operations and maneuvers. nThis study addressed the issue of the dynamic nature of headland turning of the vehicle using nthird-order spline functions. The methodologies that were developed considered both the non-holonomic naspects of vehicle motion and the constraints on the curvature of the path that is nacceptable on the headland. This automatic path creation method could generate feasible nheadland turning paths for the vehicle. Computer simulations and field tests were performed nusing a mobile robot vehicle based on a medium-size tractor. The robotic vehicle used a real-time nkinematic GPS (RTK-GPS) and a fiber optic gyroscope (FOG) as navigation sensors. nThrough the field tests, it was concluded that the developed path creation method allowed the nrobot to make turns precisely.

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 nlow cost sensors. As a navigation sensor, we used a geomagnetic direction sensor (GDS) and a ngyroscope. To increase the accuracy of these sensors outputs and navigation performance, these nsensor output were integrated based on sensor fusion technique. nIn this paper, the precise estimation method of the vehicle orientation was proposed. First, the nnoise of the GDS and inclinometer were eliminated by the adaptive line enhancer(ALE). As the nresult of inclination correction of the GDS by ALE, the corrected GDS was consistent with the nactual orientation. Second, the drift error of the gyroscope was estimated by least square nmethod(LSM). Third, these sensors output were integrated to obtain the accurate vehicle norientation. Finally, to evaluate the accuracy of the proposed estimation method of orientation, nthe automatic navigation test was carried out in the actually used field. As a result of the nautomatic navigation test, it was confirmed that proposed estimation method had the ability to nnavigate the mobile vehicle with high accuracy.

Detecting Crop Growth by a Multi-Spectral Imaging Sensor

The objective of this study is to develop a monitoring system of crop status for precision nfarming. A Multi-Spectral Imaging Sensor, which can get three wave length images(R, G and NIR) nsimultaneously, was used as the imaging sensor of this system. Field test of this system was carried nout at the paddy field with an unmanned helicopter. Geometric correction by converting from image ncoordinate to global coordinate was performed by the data of RTK-GPS and inertial sensor. After nobtaining field images by the system, leaf height, stem number and SPAD value were measured by nconventional method as ground truth. From this result, it was clear that stem number can estimate nfrom Vegetation Cover Rate which expresses the cover rate of crop within an image. Additionally, It nwas also clear that SPAD value can be estimated from Leaf Color Index calculated from the ncorrected R image.

Development of Monitoring System to Support Operations of an Unmanned Helicopter

An unmanned helicopter has been used in various applications such as crop-dusting and nremote sensing. When an unmanned helicopter flies far from an operator, it is very difficult for the noperator to recognize the position, posture and working progress of the helicopter. One of the nsolutions is to develop a monitoring system that offers the operator the state of a flying helicopter. nThe objective of this study is to develop the monitoring system to support operations of an unmanned nhelicopter. The radio transmitter was adopted to obtain the helicopter position and posture at the nbase station on the ground. The base station was composed of the radio receiver, a personal ncomputer. The received information from the helicopter was visually displayed on the developed nsoftware. The developed system is very useful for tele-operation of a helicopter. In addition, the ndynamic model of the helicopter was identified by a sub-space system identification method to ndevelop the flight simulator.

Development of Master-slave Robot System - Obstacle avoidance algorithm -

The objective of the study is to develop control system of autonomous mobile robots for farming noperation. One of the goals of this study is to investigate the use of mobile robots with as little as npossible centralized control. The key solution to this decentralization problem is to give more nautonomy to the vehicles to allow them to cope with unexpected events and obstacles, inaccurate nenvironment model, other vehicles, and so on. This high level of autonomy is achieved using nadvanced sensor-based capabilities (for localization, obstacle detection and modeling) as well as nplanning and deliberation between the robots through local communication and coordination. In nsuch context, the dynamics of the environment, the impossibility to correctly estimate the nduration of actions (the robots may be slowed down due to obstacle avoidance or re-localization nactions, and so on) prevent a central system from elaborating long or medium term efficient and nreliable detailed robot plans. In this study, we focus on obstacle avoidance of the multi-robot. nTwo robot tractors were employed as multi-robot system. Two robots have local communication nability and an own controller. The robot, which detected the moving obstacle, could take an naction to avoid clash by selecting slow-down and generating intermediate pathway.