Title: Sensor allocation and online-learning-based path planning for maritime situational awareness enhancement: A multi-agent approach

Speaker: Dr Long Nguyen

Summary:
Australia with access to large bodies of water often aims to protect its maritime transport by employing maritime surveillance systems.
However, the number of available sensors (e.g., cameras) is typically small compared to the to-be-monitored targets, and their Field of View (FOV) and range are often limited.
This makes improving the situational awareness of maritime transports challenging. To this end, we propose a method that not only distributes multiple sensors but also plans paths for them to observe multiple targets, while minimizing the time needed to achieve situational awareness. In particular, we provide a formulation of this sensor allocation and path planning problem which considers the partial awareness of the targets’ state, as well as the unawareness of the targets’ trajectories.  To solve the problem we present two algorithms: 1) a greedy algorithm for assigning sensors to targets, and 2) a distributed multi-agent path planning algorithm based on regret-matching learning.  Because a quick convergence is a requirement for algorithms developed for high mobility environments, we employ a forgetting factor to quickly converge to correlated equilibrium solutions. Experimental results show that our combined approach achieves situational awareness more quickly than related work.

Biography:
Long is a Research Fellow in the Department of Data Science and Artificial Intelligence in the Faculty of Information Technology at Monash University. His research investigates optimisation and AI-based algorithms for autonomous multi-agent systems. Long specialises in algorithms for solving planning and scheduling problems in the maritime transport, intelligent transport, and wireless communication domains. His expertise includes mixed integer linear programming, online learning, and high-performance computing. Long works with OPTIMA AI Associate Professor Markus Wagner.

Location – hybrid

In-person: Monash Uni, 20 Exhibition Wlk, Clayton-2-207-Meeting Room (Central Space)

Online: MEETING ID: 873 1557 5255; PASSWORD: 778635

WED 15 MAY 2024 16:00-17:00 (AEST, Melbourne Time)