SENSOR FUSION USING FUZZY LOGIC ENHANCED KALMAN FILTER FOR AUTONOMOUS VEHICLE GUIDANCE IN CITRUS GROVES

This article discusses the development of a sensor fusion system for guiding an autonomous vehicle through citrus grove alleyways. The sensor system for path finding consists of machine vision and laser radar. An inertial measurement unit (IMU) is used for detecting the tilt of the vehicle, and a speed sensor is used to find the travel speed. A fuzzy logic enhanced Kalman filter was developed to fuse the information from machine vision, laser radar, IMU, and speed sensor. The fused information is used to guide a vehicle. The algorithm was simulated and then implemented on a tractor guidance system. The guidance system's ability to navigate the vehicle at the middle of the path was first tested in a test path. Average errors of 1.9 cm at 3.1 m s -1 and 1.5 cm at 1.8 m s -1 were observed in the tests. A comparison was made between guiding the vehicle using the sensors independently and using fusion. Guidance based on sensor fusion was found to be more accurate than guidance using independent sensors. The guidance system was then tested in citrus grove alleyways, and average errors of 7.6 cm at 3.1 m s -1 and 9.1 cm at 1.8 m s -1 were observed. Visually, the navigation in the citrus grove alleyway was as good

[1]  Thomas A. Runkler,et al.  Model based sensor fusion with fuzzy clustering , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[2]  S. Chiu,et al.  Applying fuzzy logic to the Kalman filter divergence problem , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[3]  Robin R. Murphy,et al.  Sensor allocation for behavioral sensor fusion using min-conflict with happiness , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[4]  Paul Zarchan,et al.  Fundamentals of Kalman Filtering: A Practical Approach , 2001 .

[5]  Qin Zhang,et al.  KALMAN FILTERING OF DGPS POSITIONS FOR A PARALLEL TRACKING APPLICATION , 2002 .

[6]  Akira,et al.  AUTOMATIC GUIDANCE WITH A LASER SCANNER FOR A ROBOT TRACTOR IN AN ORCHARD , 2004 .

[7]  Jie Yang,et al.  Sensor Fusion Using Dempster-Shafer Theory , 2002 .

[8]  Anthony Stentz,et al.  Sensor fusion for autonomous outdoor navigation using neural networks , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[9]  Jerzy Z. Sasiadek,et al.  Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[10]  Vijay Subramanian,et al.  Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation , 2006 .

[11]  S. I. Cho,et al.  AUTONOMOUS SPEED SPRAYER GUIDANCE USING MACHINE VISION AND FUZZY LOGIC , 1999 .

[12]  Lawrence A. Klein,et al.  Sensor and Data Fusion Concepts and Applications , 1993 .

[13]  Kazuyuki Kobayashi,et al.  Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique , 1998, IEEE Trans. Ind. Electron..

[14]  Kazunobu Ishii,et al.  Field Automation Using Robot Tractor , 2002 .

[15]  Seishu Tojo,et al.  Machine Vision Based Guidance System for Automatic Rice Transplanters , 2003 .

[16]  Vijay Subramanian,et al.  Autonomous greenhouse sprayer vehicle using machine vision and ladar for steering control , 2005 .

[17]  Kiyoaki Matsuda,et al.  DEVELOPMENT OF LASER CROP ROW SENSOR FOR AUTOMATIC GUIDANCE SYSTEM OF IMPLEMENTS , 2004 .

[18]  R. Mobus,et al.  Multi-target multi-object tracking, sensor fusion of radar and infrared , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[19]  A.S. Paul,et al.  Dual Kalman filters for autonomous terrain aided navigation in unknown environments , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[20]  Christopher Rasmussen,et al.  Combining laser range, color, and texture cues for autonomous road following , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[21]  N. F. Toda,et al.  Divergence in the Kalman Filter , 1967 .

[22]  Yoshisada Nagasaka,et al.  Autonomous guidance for rice transplanting using global positioning and gyroscopes , 2004 .