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

自主机器人系统的发展代表了包括加拿大在内的世界发达国家的一项战略任务，以保护人口，确保适当的医疗保健和个人服务，为制造业提供竞争优势，并维持经济繁荣。然而，最先进的技术在感知和感知能力方面仍然面临局限性。在遇到的挑战中，大量数据的获取很快导致处理能力饱和，从而抢占了自主系统充分利用可用信息做出明智决策的机会。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，以开发能够进行灵巧操作的系统，试图更接近人类的能力。****提出的研究重点是发展选择性但准确的传感方法，结合机器视觉和触觉探测，以支持先进机器人应用中的自主决策系统。选择性感知被定义为基于先前获得的相同场景的知识，智能地选择下一步从哪里获取信息，目标是最小化理解场景所需的数据总量。因此，选择性传感定向收集对机器人操作的成功、安全和鲁棒性具有战略重要性的区域的信息。***主要目标是开发集成感知机器人方法，结合视觉和触觉感知来检测，选择性测量，建模和解释由先验未知性质和物理性质的物体组成的复杂环境。然后，对场景的理解将支持移动机器人或机械臂/手的操作，以进一步有选择地探测或与发现的物体进行交互。更具体地说，将扩展正式的选择性3D成像方法，以支持从多个视点获取数据，并集成不同的传感模式，并执行来自彩色和热成像、深度和触觉数据的数据融合。研究还将研究仿生图像和信号处理方法，以实现高效的选择性传感，最后，将开发原始的自适应柔性控制方案，以支持灵巧机器人在选择性多模态感官反馈下与静态、移动、刚性或可变形物体进行交互。该研究将为智能传感和自主系统的基本概念和技术创新做出重大贡献，这些系统对关键机器人应用至关重要，如辅助机器人系统、智能制造、安全和危险材料处理

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.**