Competitive Perimeter Defense of Conical Environments
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Abstract (arXiv)
We consider a perimeter defense problem in a planar conical environment in which a single vehicle, having a finite capture radius, aims to defend a concentric perimeter from mobile intruders. The intruders are arbitrarily released at the circumference of the environment and they move radially toward the perimeter with fixed speed. We present a competitive analysis approach to this problem by measuring the performance of multiple online algorithms for the vehicle against arbitrary inputs, relative to an optimal offline algorithm that has information about entire input sequence in advance. In particular, we establish two necessary conditions on the parameter space to guarantee (i) finite competitiveness of any algorithm and (ii) a competitive ratio of at least 2 for any algorithm. We then design and analyze three online algorithms and characterize parameter regimes in which they have finite competitive ratios. Specifically, our first two algorithms are provably 1, and 2-competitive, respectively, whereas our third algorithm exhibits different competitive ratios in different regimes of problem parameters. Finally, we provide a numerical plot in the parameter space to reveal additional insights into the relative performance of our algorithms.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2021.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2023Graph Neural Networks for Decentralized Multi-Agent Perimeter Defensecited by 3
- 2023A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problemcited by 0
- 2026Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutionscited by 0
- 2026Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions16 Mar 2026
- 2023A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem26 May 2023
- 2023Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense23 Jan 2023