Sensor Fusion Using Fuzzy Logic Enhanced Kalman Filter for Autonomous Vehicle Guidance in Citrus Groves

Abstract

ABSTRACT. 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 enhancedhere is a current need in the Florida citrus industry to automate citrus grove operations. This need is due to reduction in the availability of labor, rising labor cost, and potential immigration challenges. Autonomous vehicles would be an important part of an automated citrus grove. Autonomous vehicles can operate accurately for a longer duration of operation than when using a human driver. In a scenario where an automated citrus harvester is used, the operator can monitor both the harvester and the vehicle instead of being solely the driver. Based on citrus groves identified for testing the autonomous navigation of the tractor used by Subramanian et al. (2006), it was estimated that the ability to navigate in the middle of the identified citrus alleyways with an accuracy of 15 cm deviation from the center would be acceptable. In general, this estimate is dependent on the size of the vehicle, the alleyway width, layout of the citrus groves, and the application for which the autonomous vehicle is used. Numerous autonomous vehicles for agricultural applications have been described in the literature. The guidance systems developed by Noguchi et al. (2002) and Chen et al. (2003) were for operation in paddy fields for transplanting rice, whereas the guidance system de- Machine vision is a useful sensing methodology for guidance. Its long range aids in faster decision making before a hard turn is to be performed or for faster obstacle detection and is quite robust to minor variations in the path boundaries. However, it lacks the centimeter-level accuracy of laser radar in detecting the distance to close range path boundaries or obstacles. The accuracy of ladar is useful in keeping the vehicle accurately positioned in the path. However, this accuracy makes the information noisy if there are minor variations in the path boundaries. The development of an autonomous vehicle guidance system for citrus grove navigation using machine vision and laser radar (ladar) based guidance systems was discussed by Subramanian et al. (2006). Both machine vision and ladar based guidance performed with a maximum error of 6.1 cm in test paths. The machine vision based guidance system had also guided a tractor through an alleyway of a citrus grove, keeping the vehicle visually in the center of the path with no collisions with the trees. It was found that ladar based guidance was more accurate than vision based guidance in well defined paths within a speed of 3.1 m s -1 , whereas the vision based guidance was able to keep the vehicle in the middle of the path in several types of paths with less accuracy. In the approach taken in previous research, the uneven tree canopy...

Route planning for orchard operations

“…Selective examples include robots for cherry harvesting (Tanigaki et al, 2008) and apple harvesting (De-An et al, 2011). A number of navigation technologies for vehicles operating in orchards have been developed in parallel to these efforts; examples of early attempts include guidance systems based on cables (Tosaki et al, 1996), using physical contact sensors (Yekutieli and Pegna, 2002), using ultrasonic sensors combined with DGPS, and using machine vision and laser radar (Tsubota et al, 2004;Subramanian et al, 2006Subramanian et al, , 2009Barawid Jr et al, 2007). Furthermore, navigation methods from row crop systems could be efficiently applied in orchards.…”

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