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Pallet Handling System with an Autonomous Forklift for Outdoor Fields

机译:与自主叉车的托盘处理系统为室外田地

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In Japan, the aging and depopulation of its workforce are issues. Therefore, the development of autonomous agricultural robots is required for saving manpower and labor. In this paper, we described an autonomous pallet handling system for forklift, which can automatically unload and convey pallets for harvesting vegetables outdoors. Because of inserting the forks into a narrow pallet hole, accurate pallet posture estimation and accurate control of a forklift and the forks are required. The system can detect the pallet by deep learning based object detection from an image. Based on the results of object detection and measurement by horizontal 3D light detection and ranging (LiDAR), the system accurately estimates a distance as well as horizontal and vertical deviation between the forklift and the pallet in the outside field. The forklift is controlled by sliding mode control (SMC) which is robust to disturbances. Furthermore, the vertical LiDAR scans the pallet for precisely adjusting the height of the fork. The system requires the environment with no or little preparation for the automation process. We confirmed the effectiveness of the system through an experiment. The experiment is assumed that the fork-lift unloads the pallet from the vehicle as the real task of agriculture. The experimental results indicated the suitability of the system in real agricultural environments.
机译:在日本,其劳动力的衰老和分布是问题。因此,拯救人力和劳动力需要自主农业机器人的发展。在本文中,我们描述了一种用于叉车的自动托盘处理系统,可以自动卸载和传送用于在室外收获蔬菜的托盘。由于将叉插入窄托盘孔中,需要精确的托盘姿势估计和准确控制叉车和叉子。系统可以通过从图像的基于深度学习的对象检测来检测托盘。基于物体检测和通过水平3D光检测和测量的结果的结果,系统精确地估计距离和外部场中的叉车和托盘之间的水平和垂直偏差。叉车由滑动模式控制(SMC)控制,这是鲁棒的扰动。此外,垂直激光雷达扫描托盘以精确调节叉的高度。该系统要求为自动化过程提供没有或几乎没有准备的环境。我们通过实验确认了系统的有效性。假设实验叉车从车辆中卸下托盘作为农业的真正任务。实验结果表明系统在真实农业环境中的适用性。

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