Sensor Allocation and Online-Learning-based Path Planning for Maritime Situational Awareness Enhancement: A Multi-Agent Approach

Bach Long Nguyen, Anh-Dzung Doan, Tat-Jun Chin, Christophe GUETTIER, Surabhi Gupta, Estelle Parra, Ian Reid, Markus Wagner

Abstract:

Countries with access to large bodies of water often
aim to protect their 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.