Design and experiments of an automatic pipe winding machine

To solve the time-consuming and laborious problem of manual winding and unwinding water pipes by field workers during irrigation or pesticide spraying in agricultural production, an automatic pipe winding machine for winding and unwinding water pipes was designed. The guiding mechanism, pipe winding mechanism, and pipe arrangement mechanism of the pipe winding machine are designed, and the automatic deviation correction control method of pipe arrangement based on PID and the constant tension control method of pipe winding and unwinding is put forward, and the control system of the automatic pipe winding machine is developed. By making a prototype of an automatic pipe winding machine, the effects of pipe winding and unwinding and the constant tension control of the automatic winding machine are tested and analyzed. The test results show that under the condition of 4.0 km/h speed, the maximum angle error of the automatic pipe winding machine is 3.32°, the average absolute error is 0.95°, and the water pipes are arranged neatly and tightly. The maximum relative error of the water pipe tension is 9.3%, and the maximum relative error under the 0~4.0 km/h velocity step variable condition is 16.3%. The speed of the pipe winding and unwinding can adapt to the change of the vehicle’s speed automatically, and the tension of the pipe is within a reasonable range. The performance of the pipe arrangement and pipe coiling of the automatic pipe winding machine can meet the operating requirements

HeLoDL: Hedgerow Localization Based on Deep Learning

Accurate localization of hedges in 3D space is a key step in automatic pruning. However, due to the irregularity of the hedge shape, the localization accuracy based on traditional algorithms is poor. In this paper, we propose a deep learning approach based on a bird’s-eye view to overcoming this problem, which we call HeLoDL. Specifically, we first project the hedge point cloud top-down as a single image and, then, augment the image with morphological operations and rotation. Finally, we trained a convolutional neural network, HeLoDL, based on transfer learning, to regress the center axis and radius of the hedge. In addition, we propose an evaluation metric OIoU that can respond to the radius error, as well as the circle center error in an integrated way. In our test set, HeLoDL achieved an accuracy of 90.44% within the error tolerance, which greatly exceeds the 61.74% of the state-of-the-art algorithm. The average OIoU of HeLoDL is 92.65%; however, the average OIoU of the best conventional algorithm is 83.69%. Extensive experiments demonstrated that HeLoDL shows considerable accuracy in the 3D spatial localization of irregular models

<i>HeLoDL</i>: Hedgerow Localization Based on Deep Learning

Accurate localization of hedges in 3D space is a key step in automatic pruning. However, due to the irregularity of the hedge shape, the localization accuracy based on traditional algorithms is poor. In this paper, we propose a deep learning approach based on a bird’s-eye view to overcoming this problem, which we call HeLoDL. Specifically, we first project the hedge point cloud top-down as a single image and, then, augment the image with morphological operations and rotation. Finally, we trained a convolutional neural network, HeLoDL, based on transfer learning, to regress the center axis and radius of the hedge. In addition, we propose an evaluation metric OIoU that can respond to the radius error, as well as the circle center error in an integrated way. In our test set, HeLoDL achieved an accuracy of 90.44% within the error tolerance, which greatly exceeds the 61.74% of the state-of-the-art algorithm. The average OIoU of HeLoDL is 92.65%; however, the average OIoU of the best conventional algorithm is 83.69%. Extensive experiments demonstrated that HeLoDL shows considerable accuracy in the 3D spatial localization of irregular models