Enhancing robotic agents through tactile perception and multi-modal interfaces

通过触觉感知和多模式界面增强机器人代理

• 批准号: RGPIN-2020-04309
• 负责人: AlvesdeOliveira, ThiagoEustaquio
• 金额: $ 1.75万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749776
• 关键词: Enhancing; robotic; agents; through; tactile

Traditional industrial robots are equipped with highly specialized sensing, perception, and actuation suites tailored to repeat a limited set of carefully scripted assembly and manufacturing operations. Nowadays, the application of robotic agents is expanding out of the production lines and advancing towards collaborating activities, interacting with other robots, and assisting humans. These agents have the ability to explore unstructured environments while simultaneously localizing themselves, mapping their surroundings, walking and interacting with people, but they still lack the dexterity required to handle even simple objects. Unfortunately, the capabilities that make robotic agents successful on factory lines are not sufficient in unstructured settings like homes, schools, and hospitals. Applications such as health and eldercare or that take place in hazardous or high-risk environments require robotic agents to be able to complete complex dexterous manipulation operations. To perform such activities, these agents must be able to handle various objects of different materials that can be situated out of the field of view of the robot or partially obstructed by the environment or by the robot itself. Identifying and constructing models for non-occluded objects is primarily accomplished using image processing, computer vision, and machine learning methods. On the other hand, objects in cluttered environments or partially occluded during in-hand manipulation may be identified, modeled and dexterously manipulated only by agents employing tactile perception strategies. In the long term, this research program aims at developing novel tactile perception methods for the manipulation, identification, classification, and modeling of heterogeneous objects, as well as the encoding and communication of tactile information. The proposed research program also contemplates the integration of other perceptual modalities such as computer vision, myoelectric signals, and gesture recognition into collaborative and assistive robotic agents. New theoretical results will be generated with a range of applications in mind, varying from virtual and virtualized reality, human-machine interfaces and telemanipulation to smart prostheses. The results of this research program will provide solutions for practical problems in the health and industrial sectors and will help develop close research collaborations between academia and industry. A total of six graduate and five undergraduate students will be trained with the support of this grant over the next five years, contributing to Canada's highly qualified personnel. The research findings enabled by the program will be disseminated in leading refereed journals and presented at international conferences of the field.

传统的工业机器人配备了高度专业化的传感、感知和驱动套件，专门用于重复一组有限的精心编写的组装和制造操作。如今，机器人代理的应用正在从生产线扩展到协作活动，与其他机器人交互，协助人类。这些智能体有能力探索非结构化环境，同时定位自己，绘制周围环境，行走并与人互动，但它们仍然缺乏处理简单物体所需的灵活性。不幸的是，使机器人代理在工厂生产线上取得成功的能力在家庭、学校和医院等非结构化环境中是不够的。诸如健康和老年护理或发生在危险或高风险环境中的应用需要机器人代理能够完成复杂的灵巧操作。为了执行这些活动，这些代理必须能够处理各种不同材料的物体，这些物体可能位于机器人的视野之外，或者被环境或机器人本身部分阻挡。识别和构建非遮挡物体的模型主要使用图像处理、计算机视觉和机器学习方法来完成。另一方面，在混乱的环境中或在手持操作过程中部分被遮挡的物体，只能通过采用触觉感知策略的代理来识别、建模和灵巧地操作。从长远来看，本研究计划旨在开发新的触觉感知方法，用于异质物体的操作，识别，分类和建模，以及触觉信息的编码和交流。拟议的研究计划还考虑将其他感知模式（如计算机视觉、肌电信号和手势识别）整合到协作和辅助机器人代理中。新的理论结果将在一系列应用中产生，从虚拟现实和虚拟现实，人机界面和远程操作到智能假肢。这项研究计划的成果将为卫生和工业部门的实际问题提供解决方案，并将有助于发展学术界和工业界之间的密切研究合作。在未来五年内，将有六名研究生和五名本科生在这笔赠款的支持下接受培训，为加拿大的高素质人才做出贡献。该项目的研究成果将在权威期刊上发表，并在该领域的国际会议上发表。