Real-time Temporal Logic-based Planning for Multi-agent Autonomous Systems in Partially-known and Uncertain Environments

自动驾驶系统，如自动驾驶汽车或无人机（UAV），被视为解决交通运输中主要问题的解决方案（例如，减少拥堵和排放），并在消防和搜救等安全关键任务中降低人类风险。自2010年以来，自动驾驶汽车行业的投资超过2000亿美元，预计到2045年将成为一个数十亿美元的行业[31]，无人机行业也是如此[3]。然而，目前，这些系统的成功应用大多局限于简单的，孤立的环境，如笼式仓库，农村空域和专用交通路线。现实世界，尤其是城市环境，由于人为操作的系统，仍然是不确定的，并不断以部署前所未见的方式发展。在如此复杂的环境中，自动驾驶系统远不能完全自主操作，最近一系列涉及自动驾驶汽车的高调事故导致美国国家公路交通安全管理局（NHTSA）于2021年8月展开调查。我提出的研究计划的长期目标是使自主系统能够在这种不确定的，不断发展的和先验未知的环境中规划它们的运动，同时满足复杂的目标。这些可能包括以下方面的要求：1）空间（例如，避免禁飞区），2）时间（例如，森林灭火无人机每5分钟监视2个高风险区域），3）反应性（例如，消防UAV必须清除出飞机将在接下来的2分钟内洒水的区域），以及4）跨越多个代理（例如，在因烟雾而能见度有限的区域，至少2架无人机应确认是否存在其他火灾）。 这是一个挑战，既简洁和正确地指定这样的目标，在数学上合理的方式，然后自动生成运动计划的自主系统执行这些目标在复杂的现实世界的环境。该计划旨在通过在使用正式（基于逻辑的）规范语言来捕获此类操作要求以及在满足这些正式规范的运动规划方法的优化方法中取得根本性进展来解决这些技术挑战。一个特别的重点将是开发实时在线运行的有效方法，并允许在具有挑战性的环境中执行这些任务的多个自主系统之间进行协调。虽然加拿大的机器人产业发展迅速，但在毕马威2020年自动驾驶汽车准备指数中排名第12位，落后于其他技术和创新国家。该研究计划将在现实世界系统的自主系统决策基础方面取得根本性进展，并将培养学生和研究人员，使其能够在快速增长的自主系统领域发挥领导作用，包括机器人（地面和空中）和汽车行业。

Autonomous systems, such as self-driving cars or unmanned aerial vehicles (UAVs), are seen as the solution for major problems in transportation (e.g, reducing congestion and emissions) and in reducing risk for humans in safety-critical tasks such as firefighting and search-and-rescue. The self-driving car industry has seen over US$200 billion invested in it since 2010, and is expected to be a multi-billion dollar industry by 2045 [31], as is the UAV industry [3]. Currently however, successful applications of these systems are mostly limited to simple, isolated environments such as caged-off warehouses, rural airspace, and dedicated traffic routes. Real-world, especially urban environments remain uncertain due human-operated systems and constantly evolve in ways not seen prior to deployment. In such complex environments, autonomous systems are far from capable of fully autonomous operation, as seen by the recent spate of high-profile accidents involving self-driving cars which led the US National Highway Traffic Safety Administration (NHTSA) to open an investigation in August 2021. The long-term goal of my proposed research program is to enable autonomous systems to plan their motion in such uncertain, evolving, and a priori unknown environments while satisfying complex objectives. These could include requirements that are in: 1) space (e.g., avoiding a no-fly zone), 2) time (e.g., forest-fire-fighting UAVs surveilling 2 high-risk areas every 5 minutes), 3) reactive (e.g., fire-fighting UAVs must clear out of area where an aircraft will drop water on within the next 2 minutes), and 4) across multiple agents (e.g., in areas with limited visibility due to smoke, at least 2 UAVs should confirm the presence of additional fires). It is a challenge to both concisely and correctly specify such objectives in a mathematically sound manner and then to automatically generate motion plans for the autonomous systems to carry out these objectives in complex real-world environments. This program will aim to address these technical challenges by making fundamental advances in the use of formal (logic-based) specification languages for capturing such operating requirements, and in optimization-based methods for motion planning methods that satisfy these formal specifications. A particular focus will be on developing efficient methods that run online in real-time and allow for co-ordination between multiple autonomous systems carrying out these tasks in challenging environments. While Canada has a fast-growing robotics industry, it ranked 12th in KPMG's 2020 Autonomous Vehicle Readiness Index, trailing other countries in technology and innovation. This research program will make fundamental advances in the underpinnings of decision-making for autonomous systems for real-world systems and will train students and researchers to be equipped to lead in fast-growing autonomous system sectors, including robotics (ground and aerial) and the automotive industry.