IDCAIS: Inter-Defender Collision-Aware Interception Strategy against Multiple Attackers
Vishnu S. Chipade, Xinyi Wang, and Dimitra Panagou

TL;DR
This paper introduces IDCAIS, a novel multi-agent defense strategy that assigns defenders to attackers considering both interception efficiency and future collision avoidance, using MIQP and ECBF techniques.
Contribution
It presents a new defender assignment protocol that incorporates collision avoidance into interception strategies using MIQP and ECBF methods.
Findings
Effective collision-aware interception demonstrated in simulations
Reduces defender collisions while maintaining interception efficiency
Enhances multi-agent defense strategies with collision considerations
Abstract
In the prior literature on multi-agent area defense games, the assignments of the defenders to the attackers are done based on a cost metric associated only with the interception of the attackers. In contrast to that, this paper presents an Inter-Defender Collision-Aware Interception Strategy (IDCAIS) for defenders to intercept attackers in order to defend a protected area, such that the defender-to-attacker assignment protocol not only takes into account an interception-related cost but also takes into account any possible future collisions among the defenders on their optimal interception trajectories. In particular, in this paper, the defenders are assigned to intercept attackers using a mixed-integer quadratic program (MIQP) that: 1) minimizes the sum of times taken by defenders to capture the attackers under time-optimal control, as well as 2) helps eliminate or delay possible…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGuidance and Control Systems · Smart Grid Security and Resilience · Infrastructure Resilience and Vulnerability Analysis
