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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739779
• 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，大量数据的获取会迅速导致处理能力饱和，并危及明智的决策制定。这个实验研究项目的重点是机会主义，智能传感策略的发展，结合机器视觉和触觉探测，以支持自主决策系统和先进的机器人应用。机会感知被定义为基于对给定环境的部分理解，智能地选择下一步从哪里获取什么信息。最终目标是在增强自治系统决策能力的同时，最大限度地减少所需的数据总量。机会感测侧重于收集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物体的信息。提出的研究计划建立在我们最近的成功基础上：i)工业应用的RGB-D传感器的机会感测，ii)异构机器人代理之间的最佳任务分配，以及iii)可变形物体表征的视觉和触觉感测。提出的研究计划的主要目标是开发综合感知和决策机器人解决方案，将视觉，力和触觉感知与目标相结合，以检测，选择性测量，建模和解释复杂环境。对场景的理解可以支持协作移动机器人或机器人手臂/手组件的操作，以进一步选择性地探测，与物体交互和操纵物体。目标将通过以下方式实现：i)扩展我们的正式机会主义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方法来提高分布式机器人代理协调的性能和鲁棒性；iii)开发形式化方法，在有效的框架中有效地获取和组合视觉和触觉信息，以支持机器人与可变形物体的灵巧交互。本研究将为机器人应用的有效获取、表示和决策方法提供基础知识。它为无人系统获取知识的方式提供了重大突破的潜力，在一系列机器人可以干预的现实世界场景中。该技术将在实际传感和机器人平台上进行开发和验证，在各种应用环境中，包括国防/安全、协作移动机器人、灵活操纵可变形物体和先进制造。