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: 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=759687
• 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，如真空清洁和草坪修剪。与此同时，随着人工智能（AI）的不断发展，机器人有望超越这一点，与人类和物理世界进行复杂的互动。但是，除非机器人具备空间感知能力，而不仅仅是报告图像中的“什么”是“哪里”，否则这一切都不会发生。同时定位和映射（SLAM）在机器人和计算机视觉中的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，传感器对未知环境进行持续探索和映射，使机器人能够在了解该环境几何形状的同时确定其位置。SLAM正日益向密集和语义感知方向发展，旨在创造对环境的人类水平的理解。因此，被称为“空间人工智能”的多功能和先进SLAM算法的发展将对机器人技术产生重大影响，特别是在自动驾驶汽车、视觉物联网、虚拟现实和增强现实等领域。这项研究计划集中在空间人工智能，针对四个方面：1)能耗，2)运行时速度，3)健壮性和4)多功能性。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瓦的功耗），帧率的设定目标是3000 FPS。在这个帧率下，可以感知到以110公里/小时的速度移动1厘米。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，如位置和方向，几何形状，颜色，纹理，厚度，重量等，所有这些都来自有限的输入数据。这项工作是基于这样的假设：机器学习算法将有助于实现多功能性和鲁棒性，混合模拟-数字计算将有助于实现所需的能耗和运行速度。该方案提出了一种独特而新颖的方案，用于机器人和实时应用的视觉处理管道，其中硬件和软件协同设计。在机器人技术中，理解物理环境和确定机器人的运动是自主的基石。农场里的机械臂、道路上的自动驾驶汽车、医院里的移动机器人、深海里的自主潜艇，都需要对环境有准确的感知，并知道自己在环境中的位置，才能完成任务。加拿大的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高效的感知系统。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。