Fault Detection in a Swarm of Physical Robots Based on Behavioral Outlier Detection

Tarapore, Danesh; Timmis, Jon; Christensen, Anders Lyhne

doi:10.1109/tro.2019.2929015

Cited by 30 publications

(15 citation statements)

References 23 publications

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“…In more recent work, Tarapore et al ( 2017 ) proposed the use of a consensus algorithm so that the robots can collectively reach a decision on whether the behavior of a team-member can be considered normal or abnormal. This was also tested on a real robotic system (Tarapore et al, 2019 ). Qin et al ( 2014 ) provide a review on this active area of research.…”

Section: Further Challenges and Future Developments To Be Madementioning

confidence: 99%

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.

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Section: Further Challenges and Future Developments To Be Madementioning

confidence: 99%

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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“…To sample a part of this space, the fault injection scheme determines for each robot in the swarm independently a randomly chosen fault before any trials are started. The fault is chosen from the following 8 fault types previously used in studies on fault-detection in robot swarms [17], [54], which are applied at each control cycle with random variables being sampled anew:…”

Section: B Fault Recovery Analysismentioning

confidence: 99%

QED: Using Quality-Environment-Diversity to Evolve Resilient Robot Swarms

Bossens

Tarapore

2021

IEEE Trans. Evol. Computat.

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In quality-diversity algorithms, the behavioural diversity metric is a key design choice that determines the quality of the evolved archives. Although behavioural diversity is traditionally obtained by describing the observed resulting behaviour of robot controllers evaluated in a single environment, it is often more easily induced by introducing environmental diversity, i.e., by manipulating the environments in which the controllers are evaluated. This paper proposes Quality-Environment-Diversity, an algorithm that repeatedly generates a random environment according to a probability distribution over environmental features (e.g., number of obstacles, arena size and robot sensor and actuator characteristics), evaluates the controller in that environment, and then describes the controller in terms of the features of that environment, the environment descriptor. Our study compares Quality-Environment-Diversity to three baseline task-specific and generic behavioural descriptors, in 5 different robot swarm benchmark tasks. For each task, the quality of the evolved archives is assessed by their capability to provide high-performing compensatory behaviours following injection of 250 unique faults to the robots of the swarm. The evolved archives achieve a median 2-to 3-fold reduction in the impact of the faults on the performance of the swarm. A qualitative analysis of evolved archives is done by visualising the relation between diversity of compensatory behaviours, here called useful behavioural diversity, and fault recovery metrics. The resulting signatures indicate that, due to the diversity of environments inducing useful behavioural diversity, archives evolved by QED provide robot swarm controllers that are capable of recovering from high-impact faults.

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“…In the first survey on security issues in robot swarms, Higgins et al (2009) identify tampered swarm members or failing sensors, attacked or noisy communication channels, and loss of availability as the main threats to robot swarms. Tarapore et al (2015Tarapore et al ( , 2017Tarapore et al ( , 2019 address the detection of faulty robots in both simulated and physical robot swarms. Their method is based on outlier detection using the bioinspired crossregulation model.…”

Section: Security Issues In Swarm Roboticsmentioning

confidence: 99%

“…Unfortunately, real-world operation will almost certainly result in robots in the swarm that either fail (e.g., due to dust blocking their sensors) or that are malicious (e.g., due to a hacker who gains control). These failures can damage people, nature, animals, and other robots, making the reliable detection of failures a crucial task (Tarapore et al, 2019 ). We use the term Byzantine robot—based on Byzantine fault-tolerance and the Byzantine Generals Problem (Lamport et al, 1982 )—as an umbrella term to describe robots that show unintended or inconsistent behavior, independent of the underlying cause.…”

Section: Introductionmentioning

confidence: 99%

Blockchain Technology Secures Robot Swarms: A Comparison of Consensus Protocols and Their Resilience to Byzantine Robots

2020

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Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology. Bitcoin and later blockchain frameworks, such as Ethereum, have revolutionized financial transactions. These frameworks are based on decentralized databases (blockchains) that can achieve secure consensus in peer-to-peer networks. We illustrate our approach in a collective sensing scenario where robots in a swarm are controlled via blockchain-based smart contracts (decentralized protocols executed via blockchain technology) that serve as “meta-controllers” and we compare it to state-of-the-art consensus protocols using a robot swarm simulator. Additionally, we show that our blockchain-based approach can prevent attacks where robots forge a large number of identities (Sybil attacks). The developed robot-blockchain interface is released as open-source software in order to facilitate future research in blockchain-controlled robot swarms. Besides increasing security, we expect the presented approach to be important for data analysis, digital forensics, and robot-to-robot financial transactions in robot swarms.

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Fault Detection in a Swarm of Physical Robots Based on Behavioral Outlier Detection

Cited by 30 publications

References 23 publications

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

QED: Using Quality-Environment-Diversity to Evolve Resilient Robot Swarms

Blockchain Technology Secures Robot Swarms: A Comparison of Consensus Protocols and Their Resilience to Byzantine Robots

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