| 标 题： |
3-D Vision-Based Workspace with Deep Learning Capabilities for Autonomous Robot Manipulation
基于3D视觉的深度学习能力自主机器人操作工作区 |
| 作 者： |
Florian-Alexandru Brasoveanu
Andrei-Iulian Iancu
Adrian-Paul Botezatu
Adrian Burlacu |
| 关键词： |
Robotics [机器人技术]
Computer vision [ 计算机视觉]
Neural networks [ 神经网络] |
| 出版年： |
2025 |
| 文集名： |
New Advances in Mechanisms, Mechanical Transmissions and Robotics - MTM & Robotics 2024
机械、传动与机器人技术新进展——MTM与机器人2024 |
| 会议名： |
14th International Conference on Mechanisms and Mechanical Transmissions (MTM 2024), 26th International Conference on Robotics (Robotics 2024)
第14届机械与传动装置国际会议（MTM 2024），第26届机器人国际会议（Robotics 2024） |
| 届 次： |
14th |
| 摘 要： |
Process automation is a widespread concept and a popular approach to considering production facilities from the perspective of Industry 4.0. Using robots in a manufacturing line is one of the first options to increase efficiency, resilience, and precision, even in the conceptual envisioning stage of factory development. However, complete automation deprives the process of adaptability and robustness, a problem that can be overcome by using automation components that can perform behaviors close to human perception. The solution proposed in the present research is to adopt a deep learning system for a collaborative robot arm equipped with an adaptive gripper to obtain an intelligent system capable of handling various types of objects with different shapes in an unpredictable object-sorting scenario. An approach based on ARUCO markers is proposed to envelop the workspace where objects will be located, contributing to ease of use and generality. Conclusive experimental results, performance evaluation, and comments follow.
流程自动化是一个广泛的概念，也是从工业4.0视角审视生产设施的一种流行方法。在工厂开发的初步构想阶段，就可采用机器人技术来提升效率、韧性和精度。然而，完全自动化会削弱流程的适应性和鲁棒性，这一问题可通过使用能够模拟人类感知行为的自动化组件来解决。本研究提出的解决方案是为配备自适应夹具的协作机器人臂引入深度学习系统，以构建一个智能系统，能够在难以预测的物体分类场景中处理各种形状的物体。同时，提出了一种基于ARUCO标记的方案，用于包围物体所在的工作区域，从而提高系统的易用性和通用性。最后附有实验结果、性能评估及相关评论。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
京公网安备 11010202009788