Innovations for Japan agriculture based on automation and robotics

Transcription

1 The 10 th International Course on Precision Agriculture and 5th of Precision Machinery Expo Hokkaido Sapporo Innovations for Japan agriculture based on automation and robotics Prof. Noboru Noguchi Graduate School of Agriculture, Hokkaido University, Japan Member, Science Council of Japan

2 Introduction Laboratory of Vehicle Robotics Automation and robotics Precision Farming Remote sensing Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 2

3 Ratio (%) Aging in rural area in Japan Year over 65 ~ 60 ~ 39

4 Number of farmers Decrease in number of farmers Average age of farmers Average age of farmers (2010 Japanese Census of Agriculture)

5 Current situation of Japan Agriculture Aging Decrease in number of farmers Goal of self-sufficiency rate National Policy- 50% by year 2021 Increase of rice paddies that have been abandoned and are no longer cultivated Increase of largescaled farming Limit of scale-up of farm size Difficulty of adopting large machines Lack of laborforce Agriculture robots

6 Robot farming system

7 Robot Platforms in VeBots First robot (5PS) Second robot (32PS) Third robot (77PS) Forth robot (240PS) at U.S.A. Fifth robot (80PS) Sixth robot (Electric)

8 Robot tractor GPS receiver Accuracy:±2cm error,10hz GPS anntena IMU

9 Robot tractor demonstrations Planting Spraying Tillage Cultivation

10 UTM-Northing [m] Start Speed :1.5 m/s UTM-Easting [m] r.m.s. error: 2.45cm

11 Crawler robot tractor bus terminator Navigation map bus connector TBC terminal GPS receiver VR Fertilizing map Implement Bus Mission plan map Sensors Actuators ECU data logger Terminal GPS IMU PC LBS bus (DIN 9684, ISO 11783) TBC job controller tractor bus CT 801,YANMAR (59kW) Source: Prof. Auernhammer ISO BUS (ISO11783)

12 Demonstration of Crawler robot tractor

13 Northing [m] Important issues of the robot Safety Obstacle Requirements of obstacle avoidance Obtaining obstacle location and speed, Robot Easting [m] taking a measure to avoid a crash.

14 Safety (Obstacle detection sensor) Maximum distance = 80 m (d, i ) 180 o SICK LMS291 0 o Two-Dimensional Laser Scanner (LMS 291) Max. distance 8~80 [m] Response time 13 [ms] Angle resolution 1 [deg] Scanning angle 60~180 [deg] Distance error +/- 5 [cm] Direction: counterclockwise Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 14

15 Demonstration of taking an another pathway for obstacle

16 Safety (Bumper Switch) Tape Switch Rated voltage AC/DC 5V~24V Rated current AC/DC 0.3A Insulating resistance Voltage withstand Operating temp. range Operating load 100 Ohms or more 250 VDC one minute 0~50 degc 12 N Bumper switch Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 16

17 Safety (Bumper Switch) Proximity sensor Type Shielded Size M30 Detecting distance 11 mm ±15% Detectable metals Non-ferrous metals Response frequency Power supply 150 Hz 12 to 24 VDC Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 17

18 National Project for Development of Automated Agriculture and Assist System for Improving Productivity, MAFF, Japan Duration: (Five years) Budget : US$ 6.0 M (JPY 500 mil.) Main Institution: Hokkaido University Project leader: Prof. Noboru Noguchi (10 Institutions, 38 Researchers) Groups of research themes 100 R & D for fundamental technology 200 R & D for robot technology 300 Feasibility test and evaluation of economics of robot farming system 18 Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University

19 Objective To develop a robot farming system for rice, wheat and soybean fields. The robot system include a rice-planting robot, a seeding robot, a robot tractor, a robot combine harvester and various implements for the robots. The robot farming system must be economically accepted in a community. For farm area composed of scattered small farmlands (Unconsolidated farmlands) For farm area composed of accumulated large-size farmland (Consolidated farmlands) Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 19

20 S 5 11' 40" E 2.761' Integrated robot farming system based on GNSS/GIS Robot management system Tillage N 90 0' 0" E 2.750' Cultivation N 63 26' 6" E 3.354' S 0 0' 0" E 4.750' N 90 0' 0" E 6.875' Spraying Real-time monitoring & Automated documentation Seeding Harvesting Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 20

21 Robot management system Information exchange LAN Retailers Producers' cooperation Drying facility Farmers Acquisition of working condition - Wireless LAN - Packet communication Robot tractor On-site worker Robot combine harvester Rice-planting robot Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 21

22 Real-time monitoring function of robots Robot ID Show details Current robot position display Current work condition of the robot Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 22

23 Development of robot farming system Establishment of robot faming system for rice paddy Tillage & preparation Transplanting Harvesting Robot tractor Rice transplanting robot Robot combine harvester

24 Schematic diagram of a rice planting robot Transmission VRS- RTK-GPS Steering Long-mat nursery IMU Base-machine Commercialized six-rows rice planter (10.5 PS)

25 Video clip of a transplanting robot

26 Trajectory of a rice planting robot 30 a(30m-100m)including a headland Completely non-stop fertilizing-planting-herbicide application with a rice planting robot Speed:0.9m/s Travel accuracy:±3 cm, Planting accuracy:±10cm Work time:50 min/30 a

27 Robot combine harvester

28 Obstacle detection sensor Bosch and Sensing Technology Long Range Radar Video Sensor Ultrasonic Sensor Long Range Radar 2m~200m +-8deg. Vision x2 0m~80m 45deg. Ultrasonic 0.2m~2m +-60deg. Bosch LRR2 with dimensions of 73 x 70 x 60 mm (2.9 x 2.8 x 2.4 in) can be integrated almost anywhere on the front of the vehicle Bosch NIR technology used in CMOS Camera Bosch Ultrasonic Sensor type 2.1 Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 28

29 Standardization (ISO 11873) ECU and Middleware Development Meeting for International Standards Description Availability Part 1 General Standard Yes Part 2 Physical Layer Yes (revision in progress) Part 3 Data Link Layer ISO Yes Part 4 Network Layer Yes (revision in planning) Part 5 Network Management Yes (revision in planning) CAN 2.0b (29 bit) Part 6 Virtual Terminal Yes (revision in progress) Part 7 Implement Messages Yes (revision in progress) DIN 9684 Part 8 Power Train Messages Yes Part 9 (LBS) Tractor ECU Yes (revision in progress) Part 10 Task Controller No (draft status) Part 11 CAN Mobile 2.0a (11 Data bit) Dictionary Yes Part 12 Diagnostics No (in progress) Part 13 File Server Yes Part 14 Sequence Control No (in progress) Part 15 Robot Monitor and Control SAE J1939 Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 29

30 Standardization (ISO 11783) Implement ECU and Implement Bridge Implement ECU Implement Sub-network Bus Hitch VT Implement Bus Tractor Bus Transmission Task Controller Mgt. Computer Gateway Tractor ECU GPS Engine Implement ECU ISOBUS is Controller Area Network (CAN) Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 30

31 IT Agriculture (Robot Demonstration) Robot tractors autonomous navigation entering the field Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 31

32 IT Agriculture (Robot Demonstration) Wheel-type robot tractor do the headland switchback turning and go back to the barn Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 32

33 IT Agriculture (Robot Demonstration) Demonstration Video Navigation Map STV Program (TV News), Department of Bio-Production Engineering, Graduate School of Agriculture, Hokkaido University 33

34 Issues relating navigation sensors GPS cannot be used in an orchard 2-dimensional laser scanner is available RTK- GPS Laser scanner HOKKAIDO UNIVERSITY, SAPPORO, JAPAN

35 Automated Orchard Systems Orchard design Robotic tractors Water management Canopy management Remote sensing Mechanical harvest HOKKAIDO UNIVERSITY, SAPPORO, JAPAN

36 Automatic guidance using a laser scanner Automatic guidance for tree rows HOKKAIDO UNIVERSITY, SAPPORO, JAPAN

37 Automatic following vehicle using a laser scanner Leading robot Leading vehicle Follower Automatic following vehicle HOKKAIDO UNIVERSITY, SAPPORO, JAPAN

38 Cooperative work by multiple robots Dr. Qin Zhang, University of Illinois

39 Recognition of a leading vehicle Leading vehicle 180 deg Follower 2-D laser scanner

40 Video clip

41 Intelligent Farming Operation Dr. Qin Zhang, University of Illinois

42 Important factors for food production Environment Production cost Food safety

43 Prof. Mulla, Precision agriculture center, Univ. Minnesota Precision Farming Implementation WISDOM Decision Process KNOWLEDGE Optimal Resource Management Analysis and Diagnostics Remote Sensing Data Collection DATA Information Management INFORMATION

44 Sensors mounted on the helicopter Thermo tracer R-G-NIR vision sensor Laser range finder Laser scanner Omni directional camera

45 System components AI sensor GPS antenna INS and GDS Controllable pan-head PC for data logging RTK-GPS Imaging sensor (DancanTech,MS2100) Green Red NIR 540nm 660nm 810nm

46 Control of an adjustable pan-head The pan-head can be controlled about pan and tilt direction. The rotation angles were measured by encoders (resolution is 0.06 deg).

47 N orthing[m ] 推定 SPAD SPAD map (N-deficiency map) SPAD map 40 R 2 = SPAD SPAD Easting[m ]

48 Soil organic matter content map Sugar beat field Potato filed

49 Topographic data acquisition Helicopter position and posture. Rotation angles of the pan-head. Output of the laser range finder. X Z Y Ground surface Topographic data. Topographic data in the global coordinate system can be obtained from the range data.

50 Topographic map Altitude [m] Total station Topographic data of 6,223 points Over 3 hours Helicopter Topographic data of 1,371 points About 35 minutes Error of topographic map is 7 cm.

51 To generate a map on a specific agricultural area with information on the water content of the soil. The amount of water in the soil influences its specific heat. The amount of water in the soil can be estimated with measuring the surface temperature of the soil. Thermal camera Range -20~100 Accuracy ±2 Temp. Resolution Image resolution View angle 65

52 UTM-Northing [m] Soil moisture map UTM-Northing [m] 温度 [ ] X' UTM-Easting [m] 16.0 Thermal map 含水率 [%] Tiling for drainage UTM-Easting [m] Map of estimated moisture content 26.1

53 3-D map generation by SICK 建物 低地 水田 映像 貯水池 水田 水田 芝生 Sensing area

54 Topographic map 水田水田 水田 貯水池 低地 建物 低地 水田水田 水田 N 建物 N 貯水池

55 Electric compass System for generating cyber field RTK-GPS Accurate heading Laser Scanner Omni direction camera Six CCD planes Frame rate 15 [fps] [pixel]

56 Panoramic image by omni-direction camera

57 Cyber field generation Topography & Image

58 Thank you for your attention!