Next Generation Robot Perception Systems

下一代机器人感知系统

• 批准号: RGPIN-2020-04659
• 负责人: Saeedi, Sajad
• 金额: $ 2.48万
• 依托单位: Ryerson University
• 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=717240
• 关键词: Next; Generation; Robot; Perception; Systems

Robots are gradually evolving out of orderly manufacturing facilities into households, executing simple tasks such as vacuum cleaning and lawn mowing. Meanwhile, by ever-advancing artificial intelligence (AI), robots are expected to go beyond this by performing complex interactions with humans and the physical world. But this will not happen until robots are equipped with spatial awareness, beyond reporting 'what' is 'where' in an image. The research area of simultaneous localization and mapping (SLAM) in robotics and computer vision provides a geometric understanding of the environment. SLAM is a process in which an unknown environment is explored and mapped consistently by a sensor, allowing a robot to determine its position while understanding the geometry of that environment. SLAM is increasingly evolving towards dense and semantic perception, aiming to create a human-level understanding of the environment. Thus, the development of versatile and advanced SLAM algorithms, referred to as 'spatial AI', will have a great impact on robotics, and particularly in areas such as autonomous cars, visual Internet of Things, virtual reality, and augmented reality. This research proposal concentrates on spatial AI, targeting four areas: 1) energy consumption, 2) run-time speed, 3) robustness, and 4) versatility. For a robotic perception system, the set target for energy consumption is a battery charge once a day (less than 1 Watt power usage), and the set target for frame-rate is 3000 FPS. At this frame-rate, 1 cm self-motion at 110 km/h is perceived. Moreover, a robust and reliable system is crucial when robots are working with humans. Finally, a versatile system is needed to infer objects' properties such as position and orientation, geometric shape, colour, texture, thickness, weight, etc., all from limited incoming data. This work is based on the hypothesis that machine learning algorithms will help with versatility and robustness, and mixed analog-digital computation will help in achieving the desired energy consumption and run-time speed. This proposal presents a unique and novel scheme for visual processing pipelines for robotics and real-time applications where hardware and software are co-designed. In robotics, understanding the physical environment and determining the motion of the robot is the cornerstone of autonomy. A robotic arm in a farm, a self-driving car on a road, a mobile robot in a hospital, and an autonomous submarine in the deep ocean all need to have an accurate perception of the environment and to know their location within the environment to accomplish their tasks. The Canadian autonomous car and robotics industries will benefit by employing the outcomes of the proposal, i.e. efficient perception systems in terms of functionality and resource utilization. Moreover, emerging and applied research fields, e.g. applied perception and robotics in health and the environment, will have a reliable platform to build their solutions on.

机器人正逐渐从有序的生产设施发展到家庭，执行简单的任务，如真空吸尘和割草。与此同时，通过不断发展的人工智能（AI），机器人有望通过与人类和物理世界进行复杂的交互来超越这一点。但这不会发生，直到机器人配备了空间意识，超越报告“什么”是“在哪里”的图像。机器人和计算机视觉中的同时定位和映射（SLAM）研究领域提供了对环境的几何理解。SLAM是一个过程，在这个过程中，一个未知的环境被传感器探索和映射，允许机器人在理解该环境的几何形状的同时确定其位置。SLAM正日益朝着密集和语义感知的方向发展，旨在创建对环境的人类水平的理解。因此，多功能和先进的SLAM算法（称为“空间AI”）的发展将对机器人技术产生巨大影响，特别是在自动汽车、视觉物联网、虚拟现实和增强现实等领域。 该研究计划专注于空间AI，针对四个领域：1）能耗，2）运行速度，3）鲁棒性和4）通用性。对于机器人感知系统，能量消耗的设定目标是每天一次电池充电（小于1瓦的功率使用），并且帧速率的设定目标是3000 FPS。在此帧速率下，感知到110 km/h的1 cm自运动。此外，当机器人与人类一起工作时，一个强大而可靠的系统至关重要。最后，需要一个通用的系统来推断物体的属性，例如位置和方向、几何形状、颜色、纹理、厚度、重量等，全部来自有限的传入数据。这项工作是基于这样的假设，即机器学习算法将有助于实现多功能性和鲁棒性，而混合模拟数字计算将有助于实现所需的能耗和运行速度。该建议提出了一种独特的和新颖的方案，用于机器人和实时应用的视觉处理管道，其中硬件和软件是共同设计的。 在机器人技术中，理解物理环境并确定机器人的运动是自主的基石。农场中的机械臂、道路上的自动驾驶汽车、医院中的移动的机器人、深海中的自主潜艇，都需要对环境有准确的感知，知道自己在环境中的位置，才能完成任务。加拿大自动驾驶汽车和机器人行业将受益于该提案的成果，即在功能和资源利用方面的高效感知系统。此外，新兴和应用研究领域，例如健康和环境中的应用感知和机器人技术，将有一个可靠的平台来构建其解决方案。