Resilient distributed state estimation with mobile agents: overcoming Byzantine adversaries, communication losses, and intermittent measurements
Applications in environmental monitoring, surveillance and patrolling typically require a network of mobile agents to collectively gain information regarding the state of a static or dynamical process evolving over a region. However, these networks of mobile agents also introduce various challenges,...
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| Published in | Autonomous robots Vol. 43; no. 3; pp. 743 - 768 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
15.03.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0929-5593 1573-7527 1573-7527 |
| DOI | 10.1007/s10514-018-9813-7 |
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| Abstract | Applications in environmental monitoring, surveillance and patrolling typically require a network of mobile agents to collectively gain information regarding the state of a static or dynamical process evolving over a region. However, these networks of mobile agents also introduce various challenges, including intermittent observations of the dynamical process, loss of communication links due to mobility and packet drops, and the potential for malicious or faulty behavior by some of the agents. The main contribution of this paper is the development of resilient, fully-distributed, and provably correct state estimation algorithms that simultaneously account for each of the above considerations, and in turn, offer a general framework for reasoning about state estimation problems in dynamic, failure-prone and adversarial environments. Specifically, we develop a simple switched linear observer for dealing with the issue of time-varying measurement models, and resilient filtering techniques for dealing with worst-case adversarial behavior subject to time-varying communication patterns among the agents. Our approach considers both communication patterns that recur in a deterministic manner, and patterns that are induced by random packet drops. For each scenario, we identify conditions on the dynamical system, the patrols, the nominal communication network topology, and the failure models that guarantee applicability of our proposed techniques. Finally, we complement our theoretical results with detailed simulations that illustrate the efficacy of our algorithms in the presence of the technical challenges described above. |
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| AbstractList | Applications in environmental monitoring, surveillance and patrolling typically require a network of mobile agents to collectively gain information regarding the state of a static or dynamical process evolving over a region. However, these networks of mobile agents also introduce various challenges, including intermittent observations of the dynamical process, loss of communication links due to mobility and packet drops, and the potential for malicious or faulty behavior by some of the agents. The main contribution of this paper is the development of resilient, fully-distributed, and provably correct state estimation algorithms that simultaneously account for each of the above considerations, and in turn, offer a general framework for reasoning about state estimation problems in dynamic, failure-prone and adversarial environments. Specifically, we develop a simple switched linear observer for dealing with the issue of time-varying measurement models, and resilient filtering techniques for dealing with worst-case adversarial behavior subject to time-varying communication patterns among the agents. Our approach considers both communication patterns that recur in a deterministic manner, and patterns that are induced by random packet drops. For each scenario, we identify conditions on the dynamical system, the patrols, the nominal communication network topology, and the failure models that guarantee applicability of our proposed techniques. Finally, we complement our theoretical results with detailed simulations that illustrate the efficacy of our algorithms in the presence of the technical challenges described above. |
| Author | Mitra, Aritra Richards, John A. Bagchi, Saurabh Sundaram, Shreyas |
| Author_xml | – sequence: 1 givenname: Aritra orcidid: 0000-0002-0472-284X surname: Mitra fullname: Mitra, Aritra email: mitra14@purdue.edu organization: School of Electrical and Computer Engineering at Purdue University – sequence: 2 givenname: John A. surname: Richards fullname: Richards, John A. organization: Sandia National Laboratories – sequence: 3 givenname: Saurabh surname: Bagchi fullname: Bagchi, Saurabh organization: School of Electrical and Computer Engineering at Purdue University – sequence: 4 givenname: Shreyas surname: Sundaram fullname: Sundaram, Shreyas organization: School of Electrical and Computer Engineering at Purdue University |
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| Keywords | Situational awareness Distributed state estimation Byzantine attacks Resilient robotic teams |
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