Development of machine vision and laser radar based autonomous vehicle guidance system

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DEVELOPMENT OF MACHINE VISION AND LASER RADAR BASED AUTONOMOUS VEHICLE GUIDANCE SYSTEM

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Development of machine vision and laser radar based autonomous vehicle guidance system

  1. 1. SEMINAR TOPIC: Development of Machine Vision and Laser Radar Based Autonomous Vehicle Guidance System for Citrus Grove Navigation Author: Thomos F. Burks & V. Subramainan (Computer and Electronics in Agriculture, June 2006) Speaker :Ghotekar Ravikant Sainath (M.Tech 1st year) Roll No. :13AG61R16
  2. 2. • Content Introduction Objectives Material & Methods Results & Discussion Conclusions References 2
  3. 3. • INTRODUCTION Florida: 80 % citrus supply to United States Citrus harvesting: lack of manpower Citrus Industry: facing increased competition from overseas markets Need of automation & robotics in agriculture for citrus grove Current advanced navigation system in agricultural operation :GPS GPS Limitations in citrus orchard: tree canopy blocks the satellite signals Alley width is about 2.1-2.4m Tree heights vary from 4.5m-6m depending on their age (Brown, 2002) 3
  4. 4. • INTRODUCTION Potential applications of autonomous vehicle guidance • • • • • • Other applications • of autonomous • vehicle guidance in orchards ......continued Relieve operator from steering responsibility Relieve operator from speed control responsibilities reduce operator fatigue Improve cycle rate by reducing re-positioning efficiencies Harvesting Spraying Mowing Disease or nutritional deficiency monitoring 4
  5. 5. • Objectives Modify the hydraulic steering circuit of the vehicle to control the vehicle Develop a PID ( proportional integral derivative) control system for steering control Develop two algorithms for path finding, one using machine vision and another using laser radar Evaluate the performance of the machine vision guidance and the ladar guidance systems in a test path 5
  6. 6. • Material & Methods Vehicle: John Deere 6410 Machine Vision Hardware Laser Radar Computer Microcontroller Encoder Servo Valve GPS Receiver Power Supply (Inverter) RS 232 Protocol To send error info from PC to 6 microcontroller
  7. 7. • Material & Methods ….Continued Tractor with all additional attachment for autonomous guidance 7
  8. 8. • Material & Methods ….Continued System overall working 8
  9. 9. • Material & Methods     ….Continued  Machine Vision Ability of a computer to "see” Includes one or more video cameras for obtaining images for the computer to interpret With computer vision, there is always a need of physical feature like colour difference for the vision system to be able to sense effectively Vision involves many complicated algorithms for image processing and recognition Camera mounted at the front Threshold image 9
  10. 10. • Material & Methods ….Continued  Laser Radar (Ladar)  Principle: Time-of-flight Measurement  Remote sensing technology that measures distance by illuminating a target with a laser and analysing the reflected light  Distance = (Speed of Light x Time of Flight) / 2  used for ranging and obstacle avoidance Ladar Mounted on top of the tractor 10
  11. 11. • Material & Methods ….Continued EXPERIMENTAL PROCEDURE  An artificial testing path of hay bales was made  Algorithms for processing the image and ladar information had developed for citrus orchard environment & hay bales environment  Experiment were conducted on both testing path & Citrus orchard environment by both below guidance system A) Vehicle Guidance System by Machine Vision B) Autonomous Guidance System by Laser Radar System 11
  12. 12. • Material & Methods ….Continued EXPERIMENTAL PROCEDURE ON ARTIFICIAL TESTING PATH •Two types of paths: Straight path & Curved path •The hay bale width was 45 cm, length of the straight path was 70 feet & an extension of 30 feet was given to form a curved path •The path width was 3.5 m throughout the length. •Experiments conducted for three different speeds i.e. 1.8m/s, 3.1m/s, 4.4m/s •A rotating blade was attached to drawbar, which marked a line on the ground as the vehicle moved (path center traveled by the tractor) •Manually error was measured •Above procedure repeated to calculate the path root mean square error, standard deviation, maximum error and average error 12
  13. 13. • Material & Methods ….Continued VEHICLE GUIDANCE SYSTEM BY MACHINE VISION •Color: discriminator for segmenting the path •Camera calibration: To convert pixel distance to true distance •To account for the varying weather conditions: images collected over a period of 6 days in 2 months from morning to evening at half an hour intervals •Three types of conditions observed  Cloudy days: trees are darker than the path  Bright sunny days: trees are darker than the path but all pixel intensity values are elevated  Early morning and evening: when the sunlight causes the trees on one side of the row to be brighter than the path and the trees on the other side to be darker than the path • Based on this database of images, a segmentation algorithm was developed 13
  14. 14. Flowchart : Algorithm for path finding for adaptive RGB threshold value using machine vision 14
  15. 15. Fig. Machine vision results for citrus grove alleyway Fig : Raw image Fig : Tree canopy segmentation Fig : Path boundary 15
  16. 16. • Material & Methods ….Continued VEHICLE GUIDANCE BY Laser Radar Guidance System The radial distance measured by the laser radar for different angles, when driving through the test path was plotted. The discontinuities in the plot indicate the location of the hay bales The path center was determined as the center of the path, between the hay bales on either side The laser radar navigation algorithm employed a threshold distance based detection of hay bales 16
  17. 17. • Material & Methods ….Continued DESIGN OF PID CONTROL FOR STEERING CONTROL PID: Proportional integral derivative controller: attempts to minimise the error by adjusting the process control inputs 17
  18. 18. • Material & Methods ….Continued FORMULAE USED Line fitting: Least square method Pixel Distance to actual Distance Conversion 100 cm = 177 pixels Error Calculation Desired position = (Right side tree boundary + Left side tree boundary) / 2 Error = Desired position – current position Distance of the tractor centre from the hay bales Distance = Radial Distance at the hay bale * cosine (Angle at that point) 18
  19. 19. • Results & Discussion 19
  20. 20. • Results & Discussion ….Continued Performance of machine vision guidance in the straight path @ 1.8 m/s @ 3.1 m/s @ 4.4 m/s 20
  21. 21. • Results & Discussion ….Continued Performance of laser radar guidance in the straight path @ 1.8 m/s @ 3.1 m/s @ 4.4 m/s 21
  22. 22. • Results & Discussion ….Continued Performance of machine vision guidance in the curved path @ 3.1 m/s Performance of laser radar guidance in the curved path @ 3.1 m/s 22
  23. 23. • Conclusions •Machine vision and laser radar based guidance systems were developed to navigate a tractor through the alleyway of a citrus grove •A PID controller was developed and tested to control the tractor using the information from the machine vision system and laser radar •It was found that the ladar-based guidance was the better guidance sensor for straight and curved paths at speeds of up to 3.1 m/s •Machine vision-based guidance showed acceptable performance at all speeds and conditions •The average errors were below 3 cm in most cases. The maximum error was not more than 6 cm in any test run •Experiments demonstrated the accuracy of the guidance system under test path conditions and successful guidance of the tractor in a citrus orchard alleyway • Additional testing is needed to improve the performance in the citrus orchard 23
  24. 24. • References • Subramanian, V., Burks, T.F., Singh, S., 2004. Autonomous greenhouse sprayer vehicle using machine vision and ladar for steering control. Appl.Eng. Agric. 21 (5), 935–943. • Bell, T., Bevly, D., Biddinger, E., Parkinson, B.W., Rekow, A., 1998. Automatic tractor row and contour control on sloped terrain using Carrier-Phase Differential GPS. In: Proceedings of the Fourth International Conference on Precision Agriculture. • Misao, Y., 2001. An image processing based automatic steering power system. In: Proceedings of the ASAE Meeting, California, USA. • http://www.deere.com/en_US/careers/midcareer_jobs/field_robotics.html • www.wikipaedia.com • Gordon, G.P., Holmes, R.G., 1988. Laser positioning system for off-road vehicles. 24
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  27. 27. Camera • Specification: Sony FCBEX780S CCD camera with analog video output format in NTSC (National Television System Committee standard) • Camera was mounted at an angle of 45 degree to the horizontal Camera and its mount Camera mounted on the tractor 27
  28. 28. Frame Grabber It converts the analog NTSC video signal to a digital 640 x 480 RGB bitmap image 28
  29. 29. Laser Radar (Ladar) • Sick LMS-200 ladar sensor • It is a 180 degree one-dimensional sweeping laser which can measure at 1.0/0.5/0.25 degree increments with maximum range of up to 80 m • Mounted on top of the tractor cab just below the camera positioned at 45 degree to the horizontal Laser radar Laser mounted on top of the tractor 29
  30. 30. Computer • 4 GHz Pentium4 processor running Windows 2000 pro operating system • Software (to develop algorithms): Microsoft Visual C++ Computer Computer, monitor and keyboard mounted in the cabin 30
  31. 31. Microcontroller • 586 Engine controller board with a P50 expansion board from TERN Inc. • It is a C++ programmable controller board based on a 32-bit system • Function: For executing Real time-time control of the Servo valve & Encoder feedback loop. Amplifier: • To scale the control voltage from the microcontroller to the servo valve 31
  32. 32. Encoder • Stegmann Heavy Duty HD20 encoder • Function: Feeding back the wheel angle to the control system •Encoder Calibration •Tractor was positioned at a place in the lab •Angular positions were marked on the ground •From the centre position, the steering wheel was rotated to get different angles of front wheel •The number of pulses to reach different angles was noted (*…wheel centre was calibrated by trial and error) 32
  33. 33. Servo Valve 33
  34. 34. GPS Receiver • A GPS receiver was used to measure the vehicle displacement while conducting tests to determine the dynamics of the vehicle • John Deere Starfire SF2000R Differential GPS receiver was used GPS mounted at the top of the tractor 34
  35. 35. Power Supply • Inverter:  It supply required voltage to PC, the monitor and the laser radar • Cigarette lighter power source  The supply for the microcontroller and the hydraulic valve is taken from it  Provided in tractor cabin 35
  36. 36. Radial Distance measured by Laser Radar 36
  37. 37. EXPERIMENTAL PROCEDURE ON ARTIFICIAL TESTING PATH Guidance system test path (a) Fig. Straight path (b) Fig. Curved path 37
  38. 38. EXPERIMENTAL PROCEDURE ON ARTIFICIAL TESTING PATH Path traced by the rotating blade Fig. Device used to mark the tractor route on Fig. Marks on the ground indicating the path the ground traversed 38
  39. 39. Shadow image Non shadow image T H R E S H O L D E D I M A G E 39

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