Browsing by Author "Rains, G. C."

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Field testing of the autonomous cotton harvesting Roverin undefoliated cotton field
(2020 Beltwide Cotton Conferences, Austin, Texas, 2020) Fue, K. G.; Porter, W. M.; Tifton, G. A.; Barnes, E. M.; Cary, N. C.; Rains, G. C.
This study proposes the use of an autonomous rover to harvest cotton bolls before defoliation and as the bolls open. This would expand the harvest window to up to 50 days and make cotton production more profitable for farmers by picking cotton before the quality is at risk. We developed a cotton harvesting rover that is a center-articulated vehicle with an x- y picking manipulator and a combination vacuum and rotating tines end-effector to pull bolls off the plant. The rover uses a stereo camera to see rows, RTK-GPS to localize itself, fisheye camera for heading, and stereo camera to locate the cotton bolls. The SMACH library is a ROS-independent task-level architecture used to build state machines for the rapid implementation of the robot behavior. First, the GPS waypoints are obtained, and then, the rover passes over the rows while picking the cotton bolls. The navigation is controlled by a modified pure-pursuit technique together with a PID controller. Two parallel programs organize the entire rover regarding when to pick and when to navigate. While navigating, the rover looks for harvestable bolls, and when bolls are discovered, the robot will stop and pick. It will do this repetitive work until it finishes all the rows. The rover navigation had an absolute error mean of 0.189 m, a median of 0.172 m, a standard deviation of 0.137 m, and a maximum of 0.986 m. The largest errors occurred during turning around at the end of rows and were caused by wet conditions and tire slippage. The rover picked cotton bolls at the average Action Success Ratio (ASR) of 78.5% and was able to reach 95% of the bolls. Most bolls that were not picked could not be pulled into the vacuum using the rotating tines on the end-effector.
Real-time 3-D measurement of cotton boll positions using machine vision under field conditions
(2018 Beltwide Cotton Conferences, San Antonio, 2018-01) Fue, K. G.; Rains, G. C.; Porter, W. M.; Tifton, G. A.
Cotton harvesting is performed by expensive combine harvesters that hinder small to medium-size cotton farmers Advances in robotics provide an opportunity to harvest cotton using small and robust autonomous rovers that can be deployed in the field as an “army” of harvesters. This paradigm shift in cotton harvesting requires high accuracy 3D measurement of the cotton boll position under field conditions. This in-field high throughput phenotyping of cotton boll position includes real-time image acquisition, depth processing, color segmentation, features extraction and determination of cotton boll position. In this study, a 3D camera system was mounted on a research rover at 82° below the horizontal and took 720p images at the rate of 15 frames per second while the rover was moving over 2-rows of potted defoliated cotton plants. The software development kit provided by the camera manufacturer was installed and used to process and provide a disparity map of cotton bolls. The system was installed with the Robot Operating System (ROS) to provide live image frames to client computers wirelessly and in real time. Cotton boll distances from the ground were determined using a 4-step machine vision algorithm (depth processing, color segmentation, feature extraction and frame matching for position determination). The 3D camera used provided distance of the boll from the left lens and algorithms were developed to provide vertical distance from the ground and horizontal distance from the rover. Comparing the cotton boll distance above the ground with manual measurements, the system achieved an average R2 value of 99% with 9 mm RMSE when stationary and 95% with 34 mm RMSE when moving at approximately 0.64 km/h. This level of accuracy is favourable for proceeding to the next step of simultaneous localization and mapping of cotton bolls and robotic harvesting.
Visual inverse kinematics for cotton picking robot
(2019 Beltwide Cotton Conferences, New Orleans, Louisiana., 2019) Fue, K. G.; Tifton, Georgia; Porter, W. M.; Barnes, E. M.; Rains, G. C.
Fast cotton picking requires a fast-moving arm. The Cartesian arm remains the most simple and quick moving arm compared to other configurations. In this study, an investigation of the 2D Cartesian arm controlled with a stepper- drive is investigated. The arm is designed and mounted to a research rover. Two stereo cameras are installed and used to take the images of the cotton plants in two different angles. One camera is directly pointing downward while the other camera is pointing perpendicular to the row. This configuration allows the robot to view the cotton plants and bolls. The robot arm can move upward and downward or left and right. The rover uses two linear servos connected to a variable displacement pump swashplate for powering four hydraulic wheel motors and the engine accelerator linkage to move forward. The forward and backward movement of the rover makes the cotton-picking robot arm movement 3-dimensional. The downward camera gives feedback to the robotic system on the position of the arm. The rover moves forward along the row and stops whenever the cotton boll is perpendicular to the cartesian arm. The sideways camera gives an alternative view of the cotton boll that allows the robot servos to stop accurately. The arm uses vacuum suction to pick the cotton bolls. The vacuum suction end effector is mounted on the arm and pointing perpendicular to the row. In this paper, the kinematics and movement of the cotton arm and boll picking are demonstrated.
Visual row detection using pixel-based algorithm and stereo camera for cotton-picking robot
(2019 ASABE Annual International Meeting, Boston, Massachusetts, 2019) Fue, K. G.; Georgia, Tifton; Porter, W. M.; Barnes, E. M.; Rains, G. C.
Precision farming still depends heavily on RTK-GPS to navigate the rows of crops. However, GPS cannot be the only method to navigate the farm for robots to work as a “swarm” on the same farm; they also require visual systems to navigate and avoid collisions. Also, plant growth and canopy changes are not accommodated. Hence, the visual system remains a complementary method to add to the efficiency of the GPS system. In this study, optical detection of cotton rows is investigated and demonstrated. A stereo camera is used to detect the row depth, and then, a pixel- based algorithm is used to calculate and determine the upper and lower part of the canopy of the cotton rows by assuming the normal distribution of the high and low pixels. The left and right row are detected by using perspective transform and pixel-based sliding window algorithms. Then, the system determines the Bayesian score of the detection and calculates the center of the rows for smooth navigation of the cotton-picking robot. The 92.3% accuracy and F1 score of 0.951 are sufficient to deploy the algorithm for robotic operations. The deployment and testing of the robot navigation will be done in 2019.