Safe and efficient robot autonomy in unstructured and dynamic environments

自主移动机器人的潜力是巨大的，特别是如果它们能够在工作场所、公共空间和道路等非结构化环境中安全可靠地运行。他们可以用来提高生产力，向有需要的人提供援助，从事重复性和枯燥的工作，并填补从制造业到农业到卡车运输等行业的劳动力短缺。这种潜力导致了对工业的兴趣激增，许多最大的科技公司，如亚马逊、苹果、b谷歌和Shopify都在追求机器人和自动驾驶。然而，虽然机器人的自主性在不断提高，但它仍然容易出现意外故障，导致不安全和/或低效的操作。机器人如何在不确定的环境中规划它们的运动，它们如何评估移动物体和障碍物周围的安全性，它们如何协调它们在大规模工作中的努力，以及它们如何与人类监督者互动以提高性能并确保安全，这些都需要进行基础研究。推动本研究的长期目标是实现多机器人在非结构化和动态环境中的安全高效运行。这个研究项目将从两个方向来提高机器人的自主性。首先是在机器人围绕移动障碍物的运动规划方面取得根本性进展，使机器人能够更好地评估安全和风险。这包括预测未来运动风险的方法，以及通过主动感知环境的关键部分和通过其他人的行动推断环境信息来降低风险的方法。第二个方向是机器人利用人类监督者来随着时间的推移提高性能。主管通常用于在需要时远程操作机器人或使用界面为机器人提供指导。然而，目前的范例是以一种被动的方式使用监督者，在必要时填补自主机器人的不足。这个研究项目将考虑提高集体监督机器人团队的性能和安全性的问题。我们将为机器人开发算法，在需要时主动利用主管，避免主管超载，并通过学习主管的专业知识，随着时间的推移提高绩效。该研究项目将产生新的自主算法，提高机器人在非结构化环境中的能力。这些根本性的进步将使机器人能够在人类周围安全高效地运行，为工业清洁、仓储和物流、农业和自动驾驶等一系列新的机器人应用奠定基础。该研究项目将培养对加拿大具有战略重要性的不断发展和变革行业的前沿人才。

The potential of autonomous mobile robots is enormous, particularly if they can operate safely and reliably in unstructured environments such as workplaces, public spaces, and roadways. They can be used to increase productivity, provide assistance to those in need, perform repetitive and dull jobs, and to fill the labour shortages seen across industries ranging from manufacturing to agriculture, to trucking. This potential has resulted in a surge of interest in industry, with many of the biggest technology companies such as Amazon, Apple, Google, and Shopify all pursuing robotics and autonomy. However, while robot autonomy is continuously improving, it is still prone to unexpected failures, resulting in unsafe and/or inefficient operation. Fundamental research is needed into how robots plan their motion in uncertain environments, how they assess safety around moving objects and obstacles, how they coordinate their efforts to work on a large-scale, and how they interact with human supervisors to improve performance and ensure safety. The long-term goal driving this research is to achieve safe and efficient operation of multiple robots in unstructured and dynamic environments. This research program will pursue two directions to improve robot autonomy. The first is to develop fundamental advances in robot motion planning around moving obstacles that enable robots to better assess safety and risk. This includes methods to predict risk of future motions, and to reduce that risk by actively sensing critical portions of the environment and by inferring information about the environment through other's actions. The second direction is for robots to utilize human supervisors to improve performance over time. Supervisors are commonly used to (tele-) operate a robot when needed or to provide guidance to a robot using an interface. However, the current paradigm is to use supervisors in a reactive manner, filling in for the deficiencies of the autonomous robots when necessary. This research program will instead consider the problem of improving performance and safety of the collective supervisor-robot team. We will develop algorithms for robots that proactively leverage supervisors when needed, avoid overloading supervisors, and improve performance over time by learning from the supervisor's expertise. The research program will generate new algorithms for autonomy that increase the capability of robots in unstructured environments. These fundamental advances will enable robots to operate safely and efficiently around humans, forming the basis for a range of new robotic applications in industrial cleaning, warehousing and logistics, agriculture, and autonomous driving. The research program will train personnel that are positioned at the forefront of a growing and transformative industry of strategic importance to Canada.