Partition-Tolerant and Byzantine-Tolerant Decision Making for Distributed Robotic Systems With IOTA and ROS2

Keramat, Farhad; Queralta, Jorge Peña; Westerlund, Tomi

doi:10.1109/jiot.2023.3257984

Cited by 14 publications

(1 citation statement)

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“…In addition, to tackle the challenges related to sensed data sharing and energy efficiency in swarm operations for multi-drone collaboration, a secure blockchain-based technique has been proposed to counter Byzantine robots [31]. In the same way, to address issues related to Byzantine tolerance and decision making under partition tolerance, a distributed control strategy has been devised using IoT and ROS2 [32]. Likewise, to empower swarm robots in identifying malicious robots and data, a knowledge validation-based algorithm has been proposed that utilizes Hashgraph [33].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Resilient Control of Multi-Robot Systems with a Leader–Follower Model under Byzantine Attacks in the Context of the Industrial Internet of Things

Nasir,

Maiti

2024

Machines

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In this paper, an adaptive and resilient consensus control mechanism for multi-robot systems under Byzantine attack, based on sliding mode control, is proposed. The primary aim of the article is to develop a finite-time consensus control strategy even in the presence of a Byzantine attack. In the start, a finite-time consensus control mechanism is proposed to identify the essential conditions required for ensuring consensus accuracy in multi-robot systems, even when faced with Byzantine attacks using Lyapunov theory. Subsequently, a sliding mode control is combined with an adaptive technique for multi-robot systems that lack prior knowledge of Byzantine attack. Later, an attack observer is proposed to estimate the performance of multi-robot systems in the presence of a Byzantine attack. Additionally, chattering effects are mitigated by employing integral sliding mode control. As a result, resilient consensus performance of multi-robot systems can be achieved in a finite time interval. A simulation example is also presented to validate the effectiveness of the proposed model. Furthermore, we delve into the data structure of the proposed method and explore its integration with Artificial Intelligence for seamless incorporation into the Industrial Internet of Things applications.

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Section: Introductionmentioning

confidence: 99%