Selective 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选择性视觉和触觉传感

• 批准号: RGPIN-2015-05328
• 负责人: Payeur, Pierre
• 金额: $ 2.19万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683624
• 关键词: Selective; Visual; Tactile; Sensing; Autonomous

The development of autonomous robotic systems represents a strategic undertaking for developed countries around the world, including in Canada, to protect populations, ensure proper health care and personal services, provide competitive advantage to the manufacturing industry, and sustain economic prosperity. However, the most advanced technologies still face limitations in their sensing and perception capability. Among the challenges encountered, the acquisition of large amounts of data quickly leads to saturated processing capabilities that preempt full usage of the information available for autonomous systems to make educated decisions. Nevertheless, a recent trend has emerged that aims at merging measurements from vision, force and tactile sensors to develop systems capable of performing dexterous manipulation, in an attempt to get closer to human capabilities.****The proposed research focuses on the development of selective but accurate sensing methods combining machine vision and tactile probing to support autonomous decision systems in advanced robotic applications. Selective sensing is defined as intelligently selecting where to next acquire information from, based upon previously acquired knowledge of the same scene, with the goal to minimize the total amount of data required to understand the scene. As a result, selective sensing orients the collection of information over regions that are of strategic importance for the success, safety and robustness of a robotic operation.***The main objective is to develop integrated perceptual robotic approaches that combine visual and tactile perception to detect, selectively measure, model and interpret a complex environment composed of objects of a priori unknown nature and physical properties. The understanding of the scene made available will then support the operation of a mobile robot, or a robot arm/hand, to further selectively probe, or interact with the objects found.***More specifically, formal selective 3D imaging methods will be expanded to support acquisition of data from multiple viewpoints, and to integrate different sensing modalities and perform data fusion from color and thermal imaging, depth, and tactile data. The research will also investigate bio-inspired image and signal processing methods to achieve efficient selective sensing, and finally, original adaptive compliant control schemes with be developed to support dexterous robotic interaction with either static, mobile, rigid or deformable objects under selective multi-modal sensory feedback.***The research will make major contributions on fundamental concepts and technological innovations for intelligent sensing and autonomous systems that are vital to critical robotic applications, such as assistive robotic systems, intelligent manufacturing, as well as security and hazardous material manipulation.**

自主机器人系统的开发是包括加拿大在内的世界各地发达国家保护人口、确保适当的医疗保健和个人服务、为制造业提供竞争优势和维持经济繁荣的战略承诺。然而，最先进的技术在感知和感知能力方面仍然面临限制。在遇到的挑战中，大量数据的获取很快导致处理能力饱和，从而抢占可供自主系统做出明智决策的信息的充分利用。然而，最近出现了一种趋势，旨在融合视觉、力和触觉传感器的测量结果，以开发能够执行灵活操作的系统，试图更接近人类的能力。*拟议的研究重点是开发结合机器视觉和触觉探测的选择性但准确的传感方法，以支持高级机器人应用中的自主决策系统。选择性感知的定义是基于先前获得的同一场景的知识，智能地选择下一步从哪里获取信息，目标是将理解场景所需的数据量降至最低。因此，选择性传感将信息收集定向到对机器人操作的成功、安全和健壮性具有战略重要性的区域。*主要目标是开发集成的感知机器人方法，将视觉和触觉感知结合起来，以检测、选择性地测量、建模和解释由先验未知的性质和物理属性的对象组成的复杂环境。对场景的理解将支持移动机器人或机器人手臂/手的操作，以进一步选择性地探测发现的对象或与其交互。*更具体地说，正式的选择性3D成像方法将得到扩展，以支持从多个视点获取数据，并集成不同的传感模式，并执行来自颜色和热成像、深度和触觉数据的数据融合。这项研究还将研究受生物启发的图像和信号处理方法，以实现高效的选择性传感，并最终开发独创的自适应顺应控制方案，以支持机器人在选择性多模式感觉反馈下与静止、移动、刚性或可变形对象的灵活交互。*这项研究将在智能传感和自主系统的基本概念和技术创新方面做出重大贡献，这些基本概念和技术创新对关键的机器人应用至关重要，如辅助机器人系统、智能制造以及安全和危险材料操作。**