Simply: grasp!

简单来说：抓住！

• 批准号: RGPIN-2020-06157
• 负责人: Menon, Carlo
• 金额: $ 5.54万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718251
• 关键词: Simply; grasp

Grasping is a grand challenge in robotics. How to pick up objects made of different materials and shapes is of the utmost importance to truly unveil the potential of robotics for assisting humans in work and daily living activities. While many would like domestic robots with advanced pick-and-place capabilities to, for instance, unload their dishwasher, industry would highly benefit from innovative grasping solutions able to automatically pick and sort items of different shapes and materials in their warehouses, plants and stores. There is a wealth of prolific research on vision-based systems for robotic grasping. A primary focus has been on identifying the position and orientation of the target, i.e. the object to be grasped. Research also focused on finding optimal grasping locations on a given target based on the specific constraints of current grippers. Research additionally investigated how to best identify optimal manipulator trajectories to precisely reorient grippers, as current commercially available grippers require to be precisely oriented to grasp. While much progress has been made on the above-mentioned aspects, robotic grasping is often still unsuccessful in practice. In fact, currently available commercial grippers often simply cannot grasp targets with different shapes and made of different materials. Additionally, vision-based systems may fail to provide a target's precise orientation in real-world environments, such as a house, making the currently needed process of deciding how to best orient the gripper very challenging. This research program therefore aims at generating innovative grasping solutions which require minimal reorientation to grasp targets and are able to grasp most targets of different shapes and materials, even when little information about them (e.g. orientation) is available. This research has the potential to contribute to the wider adoption of robotics in our society and assist with economic growth. A grasping solution enabling most objects to be grasped has the potential to make a transformative change in robotic grasping. Results of this research program, when paired with state-of-the-art vision-guided dexterous robotic manipulators, could help e-commerce and other businesses to dramatically improve plant automatization and economic growth of our industry. Grasping solutions that are also effective when little information about the target is available have the potential to enable new industries to use robots in their facilities, even in poor lighting conditions and in the presence of occlusions. Additionally, grasping solutions requiring only minimal reorientation will enable the extensive use of affordable manipulators with few degrees of freedom guided by inexpensive cameras. These low-cost robotic systems are expected to make a strong impact in our society, as cost is a primary barrier to entry in many industrial applications. They also have a strong potential to find future daily use in our homes.

抓取是机器人领域的一大挑战。如何拾取由不同材料和形状制成的物体对于真正揭示机器人在协助人类工作和日常生活活动中的潜力至关重要。虽然许多人希望家用机器人具有先进的拾取和放置功能，例如，卸载洗碗机，但工业将从创新的抓取解决方案中受益匪浅，这些解决方案能够自动拾取和分类仓库，工厂和商店中不同形状和材料的物品。 有丰富的多产的研究基于视觉的系统，机器人抓取。一个主要的重点是识别目标的位置和方向，即要抓取的物体。研究还侧重于根据当前抓手的具体约束条件，在给定目标上找到最佳抓取位置。研究还调查了如何最好地确定最佳的机械手轨迹，以精确地重新定位夹持器，因为目前市售的夹持器需要精确地定位以抓取。 虽然在上述方面已经取得了很大的进展，但机器人抓取在实践中往往仍然是不成功的。事实上，目前可用的商业夹持器通常根本不能夹持具有不同形状和由不同材料制成的目标。此外，基于视觉的系统可能无法在现实世界的环境中提供目标的精确定向，例如房屋，这使得目前需要的决定如何最好地定向抓具的过程非常具有挑战性。因此，该研究计划旨在产生创新的抓取解决方案，这些解决方案需要最小的重新定向来抓取目标，并且能够抓取大多数不同形状和材料的目标，即使有关它们的信息很少（例如方向）。 这项研究有可能促进机器人技术在我们社会中的广泛应用，并有助于经济增长。一个能够抓取大多数物体的抓取解决方案有可能在机器人抓取中产生革命性的变化。这项研究计划的结果，当与最先进的视觉引导灵巧机器人机械手配对时，可以帮助电子商务和其他企业显着提高工厂自动化和我们行业的经济增长。在目标信息很少的情况下也有效的抓取解决方案有可能使新行业能够在其设施中使用机器人，即使是在照明条件差和存在遮挡的情况下。此外，抓取解决方案只需要最小的重新定向将使廉价的相机引导的自由度少的负担得起的机械手的广泛使用。这些低成本的机器人系统预计将对我们的社会产生巨大的影响，因为成本是许多工业应用的主要进入障碍。它们也有很大的潜力在我们的家庭中找到未来的日常使用。