Year: 2024
Type: conference-paper
Abstract: Adversarial patrolling has traditionally been explored using graph-based methods or planning and optimisation approaches. These methods inherently require environment discretisation and are computationally expensive. This paper introduces a new perspective of patrolling for reactive agents using a modified form of shepherding, typically used for large-scale swarm control. Shepherding utilises the emergent attraction-repulsion behaviours of simple agents to generate dynamic interactions between two types of agents. This paper shows that these behaviours can be utilised to model the problem space of adversarial patrolling. The emergent behaviour observed in shepherding provides a basis for more complex patrolling and responses to attackers. This paper will cover the theoretical analysis of adversarial patrolling and shepherding and propose the modified model underpinning shepherding for adversarial patrolling. The defending agent utilises a simple look-ahead strategy to opt for the best behaviour combination required to complete the adversarial patrolling task. The results demonstrate that using appropriate combinations of behaviours enables the defender to keep the attackers away from areas of interest.
Authors: Jonathan Zhou, Heba El-Fiqi, Aya Hussein
Institutions: UNSW Sydney, UNSW Canberra, University of Canberra
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406611366