Oral presentation

1. Oral presentation A technical review on GPS-based navigation systems of agricultural autonomous off-road vehicles Mohammad Taghi GHORBANI

2. Outline: • Introduction • Automation in farm vehicles • Navigation systems of autonomous vehicles • Safety issues • Conclusion

3. Introduction • declining rural labor force + need for greater efficiencies levels of in-field automation • Drive-by-wire tractors + GPS : precise mapping data over the internet significant increases in productivity • off-road applications relates to increased speed , higher precision, or extending the work period • Reducing the cost of the RTK-GPS system 50% => reducing the overall cost of autonomous weeding by 12–21% [1] • Limitation of autonomous agriculture: • the operating areas are large • surfaces may be uneven • wheel slippage • Interference with underground cables • environmental conditions may affect sensor observations • low cost systems are required

4. Automation in farm vehicles • operator-assistant and autonomous systems • (1) drive assistance (2) automatic steering and (3) autonomous machines. • slave for a master vehicle

5. Navigation systems of autonomous vehicles

6. Navigation systems of autonomous vehicles • Torii reviewed autonomous agricultural vehicles in Japan. He concluded: the combination of vision and RTK- DGPS appears to be the most promising system for the future [1]. • Mostwell-known sensors that are used in autonomous field machines are: mechanical ones, global navigation satellite systems, machine vision, ultrasonic and geomagnetic, that generate position, attitude and direction of movement information

7. dead reckoning and remote control • the machinery position is calculated by accumulating the traveling distance • substantial accumulated positioning error due to sensor drifting and vehicle slippage

8. Image processing based • [3] evaluated CCD camera based machine vision for path navigation and laser radar for obstacles avoidance in an autonomous common tractor in citrus groves. A PID control was developed to control the steering and a rotary encoder was feed backed the steering angle. In experimental test a DGPS receiver was used for determination of vehicle dynamics. • [4] developed RTK-DGPS based autonomous field navigation system including automated headland turns in sugar beet field. In order to check the camera system, crop row mapping was performed by combining vision-based row detection with RTK-DGPS information.   • Machine vision is applied for obstacle avoidance, row detection and navigation process. in comparison to dead reckoning, machine vision is a complex technique and expensive algorithm.

9. Statistical based and developed algorithms • [5] developeda dynamic path search algorithm for an intelligent navigator, to guide an autonomous agricultural tractor to track the desired path and make turns at the end of field. The installed sensors were; an RTK-GPS and FOG. • In [6], to correct position from the DGPS, the inclination by two inclinometers and heading angle, and roll/pitch angles by three vibratory gyroscopes were determined. LSM was applied to For drift error of gyroscopes and heading estimation. It is concluded that, this accuracy was equivalent to the accuracy of RTK-GPS and IMU system using high resolution A/D converter and laptop computer.

10. Statistical based and developed algorithms • [7] used optimal control. The test platform was a tractor that used RTK-GPS and IMU. Many experiments: transferring path including 90° turning, sinusoidal path and high speed guidance (3.0 m/s on straight path). Test results showed that the developed regulator has 38% better accuracy by comparing to a conventional PI controller. • [8] developed an automatic steering system between orchard alleyways, based on ultrasonic sensors. A DGPS was used for the measurement of the vehicle position. • Statistical based navigation technology decides according to past and current state of the vehicle. • uses different linear or non-linear equations for error extraction • cheap sensors and implements such as DGPS and IMU • not complex

11. Kalman filter based navigation • [9] developed an autonomous agricultural vehicle using an RTK-GPS and a set of solid-state inertial sensors. They used Kalman filter as a desired algorithm to estimate position, velocity and attitude. accuracy of this low-cost integrated positioning system can meet most tasks • [10] designed an obstacle detection and identification algorithm for an agricultural tractor using a Kalman filter. The obstacle detection system was developed, based on an RTK-GPS, IMU and laser range finder that can horizontally scan a 180° arc of up to 8 m radius with 1° resolution.

12. Kalman filter based navigation • estimates the next step (position, velocity or state) + combine cheap dead reckoning sensor by one update sensor (such as GPS) and acquire accuracy as equal to expensive RTK-GPS.

13. Safety issues • the watchdog system is required to halt all mechanical sub-systems of the tractor • it is hard to believe a completely autonomous vehicle can work in the fieldbecause of safety, control and cost problems [11] • six operating modes for safety of an autonomous vehicle [12]: • nominal safe operation • Safeoperation with warnings • Partial system shut down – mobile mode • Partial system shut down – immobile mode • Stopped – still communicating • Dead

14. Conclusion

15. Thanks for your attention!