Recent Trends in Robotic Patrolling

Author:

Basilico NicolaORCID

Abstract

AbstractPurpose of ReviewRobotic patrolling aims at protecting a physical environment by deploying a team of one or more autonomous mobile robots in it. A key problem in this scenario is characterizing and computing effective patrolling strategies that could guarantee some level of protection against different types of threats. This paper provides a survey of contributions that represent the recent research trends to deal with such a challenge.Recent FindingsStarting from a set of basic and recurring modeling landmarks, the formulations of robotic patrolling studied by current research are diverse and, to some extent, complementary. Some works propose optimal approaches where the objective function is based on the idleness induced by the patrolling strategy on locations of the environment. On-line methods focus on handling events that can dynamically alter the patrolling task. Adversarial methods, where an underlying game-theoretical interaction with an attacker is modeled, consider sophisticated attacker behaviors.SummaryThe wide spectrum of heterogenous approaches and techniques shows a common trend of moving towards more realistic models where constraints, dynamic environments, limited attacker capabilities, and richer strategy representations are introduced. The results provide complementarities and synergies towards more effective robotic patrolling systems, paving the way to a set of interesting open problems.

Publisher

Springer Science and Business Media LLC

Subject

General Engineering

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Team Orienting Problem with Dynamic Reward and Mobile Support Station;2023 IEEE International Conference on Robotics and Biomimetics (ROBIO);2023-12-04

2. Teaming behavior in adversarial scenarios;Frontiers in Control Engineering;2023-11-02

3. EM-Patroller: Entropy Maximized Multi-Robot Patrolling With Steady State Distribution Approximation;IEEE Robotics and Automation Letters;2023-09

4. Adaptive Expected Reactive algorithm for Heterogeneous Patrolling Systems based on Target Uncertainty;2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC);2023-06

5. Leonardo Drone Contest Autonomous Drone Competition: Overview, Results, and Lessons Learned from Politecnico di Milano Team;Journal of Intelligent & Robotic Systems;2023-06

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3