Robotic Perception in Extreme Environments

极端环境下的机器人感知

• 批准号: RTI-2021-00770
• 负责人: Beltrame, Giovanni
• 金额: $ 9.2万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 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=718881
• 关键词: Robotic; Perception; Extreme; Environments

Rapid advances in artificial intelligence are driving the adoption of robotics and automation in transport and logistics, providing new solutions to passenger transport, last-mile delivery, and automated warehouses. These systems carry a high level of risk to public safety and it is our duty as robotics engineers to design the safest possible systems. Since environmental perception systems are essential to any autonomous robotic system it is imperative to avoid errors in these systems. This is all the more important in extreme environments (in challenging weather, underwater environments, caves, etc.), where sensors (cameras, sonars, etc.) are subject to high levels of noise and frequently produce erroneous measurements. Tragedies involving Lion Air's Boeing 737 Max and Tesla are sad examples of the consequences of erroneous sensor and perception systems failures. The mitigation of spurious measurements has been studied in perceptual tasks such as the recording of point clouds required for remote sensing (i.e. using LiDAR) or the alignment of 3D shapes. There is an urgent public safety need to address these issues, especially with the emergence of transport and logistics robotic systems, such as autonomous flying or driving vehicles. In this research program, we will address how to autonomously reduce the adverse effects of erroneous measurements on robotic perception systems in extreme environments. There is no comprehensive solution to perception failures in robotics, especially in presence of erroneous measurements, which could be a showstopper to the deployment of robots in society. With more robust perception, robots will be safer and will be able to venture into increasingly hostile environments. Eventually, provable robustness could lead to certifiable systems, which would allow their introduction in safety-critical applications. In particular, we are interested in rapid mapping, localization, and navigation in limited-access and communication-restricted areas (e.g. space exploration, mining, etc.), where perception failures could lead to material loss and danger to human life. To achieve this goal we request a Boston Dynamics Spot, an all-terrain agile robot with an extensive sensor suite, that we plan to deploy in mapping missions in extreme environments such as caves and mountains in collaboration with space agencies in North America and Europe.

人工智能的快速发展正在推动机器人技术和自动化在运输和物流中的应用，为客运、最后一英里交付和自动化仓库提供新的解决方案。 这些系统对公共安全具有很高的风险，作为机器人工程师，我们有责任设计出尽可能安全的系统。 由于环境感知系统对于任何自主机器人系统都是必不可少的，因此必须避免这些系统中的错误。这在极端环境中（在具有挑战性的天气、水下环境、洞穴等）尤为重要，传感器（摄像头、声纳等）受到高水平噪声的影响并经常产生错误的测量。狮航的波音737 Max和特斯拉的悲剧是传感器和感知系统故障错误后果的可悲例子。 减少虚假的测量结果， 在感知任务中进行研究，例如记录遥感所需的点云（即使用LiDAR）或3D形状的对齐。公共安全迫切需要解决这些问题，特别是随着运输和物流机器人系统的出现，如自动飞行或驾驶车辆。 在这项研究计划中，我们将解决如何在极端环境中自主减少错误测量对机器人感知系统的不利影响。机器人技术中的感知故障没有全面的解决方案，特别是在存在错误测量的情况下，这可能是社会中机器人部署的一个障碍。 有了更强大的感知能力，机器人将更安全，并能够冒险进入越来越恶劣的环境。最终，可证明的鲁棒性可以导致可认证的系统，这将允许它们引入安全关键应用程序。 特别是，我们感兴趣的是在限制访问和通信限制的区域（例如空间探索，采矿等）的快速映射，定位和导航，在那里，感知失败可能导致物质损失和对人类生命的危险。 为了实现这一目标，我们请求使用波士顿动力学Spot，这是一种具有广泛传感器套件的全地形敏捷机器人，我们计划与北美和欧洲的航天机构合作，在洞穴和山脉等极端环境中进行测绘任务。