Ground based sensing systems for autonomous agricultural vehicles

Hague, T.; Marchant, J.A.; Tillett, N.D.

doi:10.1016/s0168-1699(99)00053-8

Cited by 94 publications

(55 citation statements)

References 26 publications

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“…Additional error corrections can be implemented by using alternative information sources [17], such as gyroscopes, accelerators, magnetic compasses, barometers and GPS. This process is called "sensor fusion", and in combination with calculated information from the sensor signals, it helps to resolve critical issues.…”

Section: Error Correctionmentioning

confidence: 99%

“…Although different formulas can be used to process NDVI signals and images, they always result in a grayscale or binary image with an adequate threshold. Both types of images can be used for high-resolution navigation control, and most high-resolution navigation applications use Hough transformations to detect plant rows [2,[9][10][11]17], the cross-correlation is a simpler approach in terms of processing power for a small embedded system. The combination of these images with a mask representing the plant line, for cross-correlation result in a precise position signal, which can be used to (lock-in) follow along the plant line.…”

Section: Introductionmentioning

confidence: 99%

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Precise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera

2015

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Most of the relevant technology related to precision agriculture is currently controlled by Global Positioning Systems (GPS) and uploaded map data; however, in sensitive areas with young or expensive plants, small robots are becoming more widely used in exclusive work. These robots must follow the plant lines with centimeter precision to protect plant growth. For cases in which GPS fails, a camera-based solution is often used for navigation because of the system cost and simplicity. The low-cost plant camera presented here generates images in which plants are contrasted against the soil, thus enabling the use of simple cross-correlation functions to establish high-resolution navigation control in the centimeter range. Based on the foresight provided by images from in front of the vehicle, robust vehicle control can be established without any dead time; as a result, off-loading the main robot control and overshooting can be avoided.

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Section: Error Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Precise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera

2015

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“…Machine vision (optical sensors) is described in Hague et al [34] as a cheap, fast, and powerful sensing method. Still, it has its drawbacks, especially in an outdoor environment, where it has to handle natural lighting conditions.…”

Section: Sensor Techniques For Perception Of Environmentmentioning

confidence: 99%

“…Direct sunlight, shadows, and clouds affect the observed data and artificial light creates shadows. Hague et al [34] state other difficulties, such as storing and processing data, extracting useful information from the images, especially when dealing with natural objects. Dunn and Liang [17] present low-resolution vision modules for obstacle detection and avoidance.…”

Section: Sensor Techniques For Perception Of Environmentmentioning

confidence: 99%

Requirements and system design for a robot performing selective cleaning in young forest stands

Vestlund

Hellström

2006

Journal of Terramechanics

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Cleaning is a silvicultural tending operation, primarily aimed at improving the growing conditions of the remaining trees in young stands (ca. 3 m of height). The cost of cleaning has increased in comparison to other forest operations, and the annually cleaned area has decreased in Sweden. Therefore, cleaning with robots might be the key to improve profitability. This paper aims at assessing some design requirements, and suggesting an architecture for a robot cleaning in young forest stands, based on reviewed literature and our own research. The results of cleaning performed by robots have to reach acceptable results and be done at a competitive cost. The robot has to find, select, and handle trees in the whole assigned area according to given instructions. Furthermore, it must be safe for humans, capable of moving safely within the forest environment, and be able to handle snow and other prevalent boreal weather conditions. The vehicleÕs size and mass are of importance, and bear on its ability to manoeuvre among remaining stems. Generally, the robot must be capable of operating independently and unattended for several hours in a dynamic and non-deterministic environment. Obstacle avoidance and target identification are identified as the most difficult problems. Machine vision, radar, and laser scanners are promising techniques for both obstacle avoid- Journal of Terramechanicsance, tree identification, and tool control. The proposed architecture is based on a hybrid between the reactive and the hierarchical robot paradigms.

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“…The fitted lines were defined by an offset and a slope parameters, in direct relation with the state variable of the vehicle location (the lateral position of the tractor, the heading angle and the steering angle). Hague et al (2000) presented an experimental autonomous vehicle guided by a machine vision system and additional sensors (odometers and inertial sensors). This vehicle was able to follow cauliflowers rows and to ensure a dead reckoning.…”

Section: Introductionmentioning

confidence: 99%

A computer-vision based precision seed drill guidance assistance

Leemans

Destain

2007

Computers and Electronics in Agriculture

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This paper presents a control mechanism aiming to position seed drills relative to the previous lines, while sowing. The position was measured by a machine vision system and used in a feedback control loop. An articulated mechanism was used to ensure the lateral displacement of the drill relative to the tractor. The behaviour of the whole outfit was studied during several field tests. The standard deviation of the error, measured as the difference between the observed inter-row distance and its set value, was 23 mm and its range was less than 100 mm, which was sufficient to fulfil the requirements of the application. Sources of systematic errors were also identified as linked to the geometric considerations. Their correction requires an accurate mounting of the camera, which may be possible for a serial montage.

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Ground based sensing systems for autonomous agricultural vehicles

Cited by 94 publications

References 26 publications

Precise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera

Precise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera

Requirements and system design for a robot performing selective cleaning in young forest stands

A computer-vision based precision seed drill guidance assistance

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