Resilience in multi-robot target tracking through reconfiguration

Ramachandran, Ragesh K.; Fronda, Nicole; Sukhatme, Gaurav S.

doi:10.1109/icra40945.2020.9196961

Cited by 22 publications

(16 citation statements)

References 19 publications

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“…3. Fusion under abnormal/non-standard WSN conditions: sensor failure (Ramachandran et al, 2020), malicious nodes (Vempaty et al, 2013), timevarying topology and number of nodes, heterogeneous sensor configurations (Hu et al, 2020;Yan et al, 2020a), and so on.…”

Section: More Open Issuesmentioning

confidence: 99%

Recent advances in multisensor multitarget tracking using random finite set

Zhu

et al. 2021

Front Inform Technol Electron Eng

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In this study, we provide an overview of recent advances in multisensor multitarget tracking based on the random finite set (RFS) approach. The fusion that plays a fundamental role in multisensor filtering is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion, which share and fuse local measurements and posterior densities between sensors, respectively. Important properties of each fusion rule including the optimality and sub-optimality are presented. In particular, two robust multitarget density-averaging approaches, arithmetic-and geometric-average fusion, are addressed in detail for various RFSs. Relevant research topics and remaining challenges are highlighted.

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Section: More Open Issuesmentioning

confidence: 99%

Recent advances in multisensor multitarget tracking using random finite set

Zhu

et al. 2021

Front Inform Technol Electron Eng

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“…There has been significant work focusing on making individual robots resilient [33,34]. Recently, there is a trend on investigating resilience in multi-robot teams, often grounded in tasks such as formation control [14,18,35,36,37,38,39,40,41,42,43,44], wireless communication [13,45], state estimation [21,46,47,48], data collection [4,15,49,50,51], attack-defense games [52,53,54], and adaptive reconfiguration [19,20,55,56]. A summary of these applications is found in Table 1.…”

Section: Resilient Coordinationmentioning

confidence: 99%

“…In addition to designing coordination algorithms that withstand attacks or failures [4,50,51,69], we also need resilient reconfiguration approach that enables robot teams to adaptively recover after attacks or faults [19,20,55,56]. Ramachandran et al studied the problem of maintaining resource availability in a network of multiple robots [19] in such conditions.…”

Section: Resilient Reconfiguration Via Optimizationmentioning

confidence: 99%

Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

Zhou

Tokekar

2021

Curr Robot Rep

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Purpose of Review Deploying a team of robots that can carefully coordinate their actions can make the entire system robust to individual failures. In this report, we review recent algorithmic development in making multi-robot systems robust to environmental uncertainties, failures, and adversarial attacks.Recent Findings We find the following three trends in the recent research in the area of multi-robot coordination: (1) resilient coordination to either withstand failures and/or attack or recover from failures/attacks; (2) risk-aware coordination to manage the trade-off risk and reward, where the risk stems due to environmental uncertainty; (3) Graph neural networks based coordination to learn decentralized multi-robot coordination policies. These algorithms have been applied to tasks such as formation control, task assignment and scheduling, search and planning, and informative data collection. SummaryIn order for multi-robot systems to become practical, we need coordination algorithms that can scale to large teams of robots dealing with dynamically changing, failure-prone, contested, and uncertain environments. There has been significant recent research on multi-robot coordination that has contributed resilient and risk-aware algorithms to deal with these issues and reduce the gap between theory and practice. Learning-based approaches have been seen to be promising, especially since they can learn who, when, and how to communicate for effective coordination. However, these algorithms have also been shown to be vulnerable to adversarial attacks, and as such developing learning-based coordination strategies that are resilient to such attacks and robust to uncertainties is an important open area of research.

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“…1. For example, [11] focuses on cooperative planning for robots to capture multiple evaders that move adversarially to escape, instead of considering attacks that may disable robots' sensors or communications; [12] focuses on distributed state estimation against adversaries that broadcast deceptive sensing information to mislead robots, instead of securing against adversaries that may deactivate the sensors of robots; [14] focuses on adaptive resource reconfiguration to recover robots' resources (e.g., sensing capabilities) from failures, instead of robust algorithms that aim to withstand attacks or failures.…”

Section: Communication Attack/failurementioning

confidence: 99%

Robust Multi-Robot Active Target Tracking Against Sensing and Communication Attacks

Zhou¹,

Kumar²

2021

Preprint

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The problem of multi-robot target tracking asks for actively planning the joint motion of robots to track targets. In this paper, we focus on such target tracking problems in adversarial environments, where attacks or failures may deactivate robots' sensors and communications. In contrast to the previous works that consider no attacks or sensing attacks only, we formalize the first robust multi-robot tracking framework that accounts for any fixed numbers of worst-case sensing and communication attacks. To secure against such attacks, we design the first robust planning algorithm, named Robust Active Target Tracking (RATT), which approximates the communication attacks to equivalent sensing attacks and then optimizes against the approximated and original sensing attacks. We show that RATT provides provable suboptimality bounds on the tracking quality for any non-decreasing objective function. Our analysis utilizes the notations of curvature for set functions introduced in combinatorial optimization. In addition, RATT runs in polynomial time and terminates with the same running time as state-ofthe-art algorithms for (non-robust) target tracking. Finally, we evaluate RATT with both qualitative and quantitative simulations across various scenarios. In the evaluations, RATT exhibits a tracking quality that is near-optimal and superior to varying non-robust heuristics. We also demonstrate RATT's superiority and robustness against varying attack models (e.g.. worst-case and bounded rational attacks).

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Resilience in multi-robot target tracking through reconfiguration

Cited by 22 publications

References 19 publications

Recent advances in multisensor multitarget tracking using random finite set

Recent advances in multisensor multitarget tracking using random finite set

Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

Robust Multi-Robot Active Target Tracking Against Sensing and Communication Attacks

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