Opportunistic Visual and Tactile Sensing for Autonomous Systems Perception and Robotic Applications

用于自主系统感知和机器人应用的机会视觉和触觉传感

• 批准号: RGPIN-2020-04307
• 负责人: Payeur, Pierre
• 金额: $ 2.04万
• 依托单位: University of Ottawa
• 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=715370
• 关键词: Opportunistic; Visual; Tactile; Sensing; Autonomous

Autonomous robotic systems represent a strategic avenue to protect populations, support health care, and sustain economic prosperity. However, state-of-the-art robotic technologies still face limitations in their sensing and perception capability that limit their actual level of autonomy. Among the challenges encountered, the acquisition of large amounts of data quickly leads to saturation of processing capabilities and compromises educated decision making. This experimental research program focuses on the development of opportunistic, intelligent sensing strategies that combine machine vision and tactile probing to support autonomous decision systems and advanced robotic applications. Opportunistic sensing is defined as intelligently selecting what information to acquire next and from where, strategically based upon a partial understanding of a given environment. The ultimate goal is to minimize the total amount of data required while enhancing decision capabilities of an autonomous system. Opportunistic sensing focuses the collection of information over regions or objects that are of strategic importance for the success, safety, precision and robustness of a robotic operation. The proposed research program builds upon our recent successes in i) opportunistic sensing with RGB-D sensors for industrial applications, ii) optimal task allocation among heterogeneous robotic agents, and iii) visual and tactile sensing for the characterization of deformable objects. The main objective of the proposed research program is to develop integrated perceptual and decisional robotic solutions that combine vision, force and tactile perception with the goal to detect, selectively measure, model and interpret complex environments. Understanding of the scene can support the operation of collaborative mobile robots, or a robot arm/hand assembly to further selectively probe, interact with, and manipulate objects. The objective will be achieved by: i) expanding our formal opportunistic 3D imaging methods to tackle sensors' mobility issues; ii) leveraging state-of-the-art machine learning and visual attention methods to increase performance and robustness for the coordination of distributed robotic agents, and iii) developing formal methods to efficiently acquire and combine visual and tactile information in an efficient framework to support dexterous robotic interaction with deformable objects. This research will contribute fundamental knowledge related to efficient acquisition, representation and decisional methods for robotic applications. It offers the potential for significant breakthroughs in the way knowledge is acquired by unmanned systems, over a range of real-world scenarios where robots can intervene. The technology will be developed and validated on actual sensing and robotic platforms, in various contexts of application including defence/security, collaborative mobile robots, dexterous manipulation of deformable objects, and advanced manufacturing.

自主机器人系统代表了保护人口、支持医疗保健和维持经济繁荣的战略途径。然而，最先进的机器人技术仍然面临着传感和感知能力的限制，这限制了它们的实际自主水平。在遇到的挑战中，大量数据的获取很快导致处理能力饱和，并损害了明智的决策。 该实验研究计划的重点是开发机会主义的智能传感策略，该策略将联合收割机机器视觉和触觉探测相结合，以支持自主决策系统和先进的机器人应用。直觉感知被定义为基于对给定环境的部分理解，从战略上智能地选择接下来从哪里获取什么信息。最终目标是最大限度地减少所需的数据总量，同时增强自治系统的决策能力。机器人传感集中在对机器人操作的成功、安全、精确和鲁棒性具有战略重要性的区域或对象上的信息收集。 拟议的研究计划建立在我们最近在以下方面取得的成功的基础上：i）使用RGB-D传感器进行工业应用的机会主义传感，ii）异构机器人代理之间的最佳任务分配，以及iii）用于可变形物体表征的视觉和触觉传感。拟议的研究计划的主要目标是开发集成的感知和决策机器人解决方案，该解决方案将联合收割机视觉，力和触觉感知与目标相结合，以检测，选择性测量，建模和解释复杂的环境。对场景的理解可以支持协作移动的机器人或机器人臂/手组件的操作，以进一步选择性地探测、交互和操纵对象。这一目标将通过以下方式实现：i）扩展我们正式的机会主义3D成像方法，以解决传感器的移动性问题; ii）利用最先进的机器学习和视觉注意力方法来提高分布式机器人代理协调的性能和鲁棒性，以及iii）开发形式化的方法，以有效地获取和联合收割机视觉和触觉信息的有效框架，以支持灵巧机器人与可变形物体的相互作用。 这项研究将有助于机器人应用的有效获取，表示和决策方法相关的基础知识。它为无人系统获取知识的方式提供了重大突破的潜力，在机器人可以干预的一系列现实世界场景中。该技术将在实际的传感和机器人平台上进行开发和验证，适用于各种应用环境，包括国防/安全、协作移动的机器人、可变形物体的灵巧操纵和先进制造。