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

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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.