학술논문

Multivehicle Perimeter Defense in Conical Environments
Document Type
Periodical
Source
IEEE Transactions on Robotics IEEE Trans. Robot. Robotics, IEEE Transactions on. 40:1439-1456 2024
Subject
Robotics and Control Systems
Computing and Processing
Components, Circuits, Devices and Systems
Games
Task analysis
Routing
Contracts
Wildlife
Vehicle dynamics
Surveillance
Agent based systems
cooperative agents
online algorithms
Language
ISSN
1552-3098
1941-0468
Abstract
In this article, we consider a perimeter defense problem in a planar conical environment in which $M$ identical vehicles, each having a finite capture radius, seek to defend a concentric perimeter from mobile intruders. The intruders are released at the circumference of the environment at arbitrary times and in any number. Upon release, each intruder moves radially toward the perimeter with fixed speed. We provide a worst-case analysis of this problem. Specifically, we present a competitive analysis approach to this problem by measuring the performance of decentralized and cooperative online algorithms for the vehicles against arbitrary inputs, relative to an optimal offline algorithm that has information about entire intruder release sequence in advance. We first establish a necessary condition on the problem parameters that guarantees finite competitiveness of any algorithm. We then design and analyze three decentralized and two cooperative online algorithms and characterize parameter regimes in which they have finite competitive ratios. Specifically, our first two decentralized algorithms are provably 1 and 2-competitive, respectively, whereas our third decentralized algorithm exhibits different competitive ratios in different regimes of problem parameters. Our first cooperative algorithm is 1.5-competitive and our second cooperative algorithm exhibits different competitive ratios in different regimes of problem parameters. Finally, we provide multiple numerical plots in the parameter space to reveal additional insights into the relative performance of our algorithms and discuss an extension to the case of heterogeneous vehicles.