Localization and control of an autonomous orchard vehicle

Bayar, Gökhan; Bergerman, Marcel; Koku, Ahmet Buğra; Konukseven, Erhan İlhan

doi:10.1016/j.compag.2015.05.015

Cited by 81 publications

(27 citation statements)

References 22 publications

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“…Orchard operations such as mowing, spraying, pruning and harvesting are usually performed with a forward vehicle velocity around 1.0 m s -1 (Davis 2012, Bayar et al, 2015. When the forward velocity of a four-wheeled vehicle (the rear and front wheels are used for driving and steering, respectively) is less than 1.0 m s -1 , sideslip angle effects may be neglected (Joanny et al, 2003).…”

Section: Sideslip Angle Estimationmentioning

confidence: 99%

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Improving the trajectory tracking performance of autonomous orchard vehicles using wheel slip compensation

Bayar

Bergerman

Konukseven

et al. 2016

Biosystems Engineering

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In this paper, the effects of wheel slip estimation and compensation of trajectory tracking for orchard applications were investigated. A slippage estimator was developed and adapted into a car-like robot model. Steering and velocity commands were generated using a model-based control approach. The whole system was implemented and tested on an autonomous orchard vehicle that has steerable front wheels and actuated rear wheels. A high accuracy positioning system was used to estimate the longitudinal and lateral slip velocities while the vehicle is moving. A laser scanning range finder was placed at the front centre of the vehicle, which was used to detect rows of trees in the orchard. Procedures were first tested in a non-flat but open space, which was covered with snow. Then it was tested on an experimental orchard where the surface was covered with heavy mud and the vehicle was expected to follow trajectories that span multiple rows in the orchard. The vehicle detected individual trees as well as rows of trees to track the centre of each row and manoeuvred from one row to the next. The experimental results showed that trajectory tracking performance of the vehicle was enhanced via integrating a slippage estimator into the system model. Furthermore, using the slippage estimation in the system model increased the accuracy, repeatability and performance of the control system. Keywords Autonomous orchard vehicle; Slippage estimation; Row following; Turning. Nomenclature RTK-GPS real time kinematic-Global Positioning System WD wheel drive ROS Robot Operating System x, y coordinate system in the centre of the rear axle of the vehicle X, Y vehicle's current position

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Section: Sideslip Angle Estimationmentioning

confidence: 99%

“…The vehicle localisation block shown in Figure 4 is fed by the data coming from the laser scanner, wheel and steering encoders, and position feedback block. The detail of this block is described in Bayar et al (2015).…”

Section: Control System Structurementioning

confidence: 99%

Improving the trajectory tracking performance of autonomous orchard vehicles using wheel slip compensation

Bayar

Bergerman

Konukseven

et al. 2016

Biosystems Engineering

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“…Bayar et al [26] used a UGV navigating in fruit orchards based on data retrieved from a laser scanner, and wheel and steering encoders. The optimal navigation minimized the total costs, thus demonstrating the potential for the use of UGVs in agriculture.…”

Section: Physical Space: Real-world Implementationmentioning

confidence: 99%

AgROS: A Robot Operating System Based Emulation Tool for Agricultural Robotics

2019

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This research aims to develop a farm management emulation tool that enables agrifood producers to effectively introduce advanced digital technologies, like intelligent and autonomous unmanned ground vehicles (UGVs), in real-world field operations. To that end, we first provide a critical taxonomy of studies investigating agricultural robotic systems with regard to: (i) the analysis approach, i.e., simulation, emulation, real-world implementation; (ii) farming operations; and (iii) the farming type. Our analysis demonstrates that simulation and emulation modelling have been extensively applied to study advanced agricultural machinery while the majority of the extant research efforts focuses on harvesting/picking/mowing and fertilizing/spraying activities; most studies consider a generic agricultural layout. Thereafter, we developed AgROS, an emulation tool based on the Robot Operating System, which could be used for assessing the efficiency of real-world robot systems in customized fields. The AgROS allows farmers to select their actual field from a map layout, import the landscape of the field, add characteristics of the actual agricultural layout (e.g., trees, static objects), select an agricultural robot from a predefined list of commercial systems, import the selected UGV into the emulation environment, and test the robot’s performance in a quasi-real-world environment. AgROS supports farmers in the ex-ante analysis and performance evaluation of robotized precision farming operations while lays the foundations for realizing “digital twins” in agriculture.

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“…Therefore, the key to increasing the precision and robustness of a navigation system lies in the combination of LiDAR and Inertial Measurement Units (IMUs) [19,29]. Bayar [30] et al have made full use of the data from LiDAR, wheels, and the steering encoder. A motion model, including wheel side slip, was constructed to calculate the vehicle’s speed and its steering instructions.…”

Section: Introductionmentioning

confidence: 99%

A Rubber-Tapping Robot Forest Navigation and Information Collection System Based on 2D LiDAR and a Gyroscope

Zhang

Yong

Chen

et al. 2019

Sensors

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Natural rubber is widely used in human life because of its excellent quality. At present, manual tapping is still the main way to obtain natural rubber. There is a sore need for intelligent tapping devices in the tapping industry, and the autonomous navigation technique is of great importance to make rubber-tapping devices intelligent. To realize the autonomous navigation of the intelligent rubber-tapping platform and to collect information on a rubber forest, the sparse point cloud data of tree trunks are extracted by the low-cost LiDAR and a gyroscope through the clustering method. The point cloud is fitted into circles by the Gauss–Newton method to obtain the center point of each tree. Then, these center points are threaded through the Least Squares method to obtain the straight line, which is regarded as the navigation path of the robot in this forest. Moreover, the Extended Kalman Filter (EKF) algorithm is adopted to obtain the robot’s position. In a forest with different row spacings and plant spacings, the heading error and lateral error of this robot are analyzed and a Fuzzy Controller is applied for the following activities: walking along one row with a fixed lateral distance, stopping at fixed points, turning from one row into another, and collecting information on plant spacing, row spacing, and trees’ diameters. Then, according to the collected information, each tree’s position is calculated, and the geometric feature map is constructed. In a forest with different row spacings and plant spacings, three repeated tests have been carried out at an initial speed of 0.3 m/s. The results show that the Root Mean Square (RMS) lateral errors are less than 10.32 cm, which shows that the proposed navigation method provides great path tracking. The fixed-point stopping range of the robot can meet the requirements for automatic rubber tapping of the mechanical arm, and the average stopping error is 12.08 cm. In the geometric feature map constructed by collecting information, the RMS radius errors are less than 0.66 cm, and the RMS plant spacing errors are less than 11.31 cm. These results show that the method for collecting information and constructing a map recursively in the process of navigation proposed in the paper provides a solution for forest information collection. The method provides a low-cost, real-time, and stable solution for forest navigation of automatic rubber tapping equipment, and the collected information not only assists the automatic tapping equipment to plan the tapping path, but also provides a basis for the informationization and precise management of a rubber plantation.

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Localization and control of an autonomous orchard vehicle

Cited by 81 publications

References 22 publications

Improving the trajectory tracking performance of autonomous orchard vehicles using wheel slip compensation

Improving the trajectory tracking performance of autonomous orchard vehicles using wheel slip compensation

AgROS: A Robot Operating System Based Emulation Tool for Agricultural Robotics

A Rubber-Tapping Robot Forest Navigation and Information Collection System Based on 2D LiDAR and a Gyroscope

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