Multi-layered grasp and manipulation system for application in uncertain environments

适用于不确定环境的多层抓取和操纵系统

• 批准号: RGPIN-2017-05216
• 负责人: Moussa, Medhat
• 金额: $ 1.75万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753192
• 关键词: Multi; layered; grasp; manipulation; system

After decades of using robots in mostly "robot-friendly” industrial environments which are typically structured, uncluttered and well understood, robots today have been successfully fielded in "human-friendly " environments such as museums, office buildings, department stores, and homes. These environments are typically uncertain, unstructured and mostly cluttered. Grasping and manipulation is a central skill for a robot. It can impact its environment using its gripper. The field of robot grasping and manipulation is reaching an important milestone. In recent years, many robots have demonstrated that they can reliably perform basic grasps on unknown objects in unstructured environments. However, these robots are still far away from being capable of human-level manipulation skills such as in-hand manipulation of objects, interactions with non-rigid objects, and multi-object tasks such as stacking and tool-usage. As such, advanced manipulations that involve interacting with uncertain real-world environments pose major problems for current approaches and traditional methods that depend on accurate models of the robot and its surroundings. The objective of this research proposal is to develop an integrated system that can plan, grasp, and learn how to grasp objects in uncertain environments. This work can have a significant impact on many applications ranging from manufacturing to precision agriculture.

经过几十年在大多数“机器人友好”的工业环境中使用机器人，这些工业环境通常是结构化的，整洁的和易于理解的，今天的机器人已经成功地应用于“人类友好”的环境，如博物馆，办公楼，百货公司和家庭。这些环境通常是不确定的，非结构化的，大多是杂乱的。抓取和操纵是机器人的核心技能。它可以用它的抓手影响周围的环境。机器人抓取和操作领域正在达到一个重要的里程碑。近年来，许多机器人已经证明，他们可以可靠地执行在非结构化环境中对未知物体的基本抓取。然而，这些机器人仍然远远没有达到人类水平的操作技能，例如手操作物体，与非刚性物体的交互以及堆叠和工具使用等多物体任务。因此，涉及与不确定的现实世界环境进行交互的高级操作对当前方法和依赖于机器人及其周围环境的精确模型的传统方法构成了主要问题。本研究的目标是开发一个集成系统，可以计划，把握，并学习如何把握不确定环境中的对象。这项工作可以对从制造业到精准农业的许多应用产生重大影响。