On the robustness of consensus-based behaviors for robot swarms

Moussa, Majda; Beltrame, Giovanni

doi:10.1007/s11721-020-00183-1

Cited by 9 publications

(5 citation statements)

References 46 publications

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“…Consensus algorithms can be widely used in many applications related to blockchain systems, such as wireless sensor networks, machine learning, swarm robotics, etc. [24][25][26][27]. There are various kinds of consensus algorithms.…”

Section: Blockchain Systemmentioning

confidence: 99%

“…The research on these consensus algorithms extends to wireless sensor networks, machine learning, and other fields. Moussa et al [24] proposed using a formal software engineering technique, statistical model checking, to model and assess the robustness of consensus-based behaviors from a communication standpoint in robot collaboration. Bondugula et al [25] proposed a novel weighted consensus model to minimize the number of false negatives and false positives without compromising accuracy.…”

Section: Blockchain Systemmentioning

confidence: 99%

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An Evolutionary Game Theory-Based Method to Mitigate Block Withholding Attack in Blockchain System

et al. 2023

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Consensus algorithms are the essential components of blockchain systems. They guarantee the blockchain’s fault tolerance and security. The Proof of Work (PoW) consensus algorithm is one of the most widely used consensus algorithms in blockchain systems, using computational puzzles to enable mining pools to compete for block rewards. However, this excessive competition for computational power will bring security threats to blockchain systems. A block withholding (BWH) attack is one of the most critical security threats blockchain systems face. A BWH attack obtains the reward of illegal block extraction by replacing full proof with partial mining proof. However, the current research on the BWH game could be more extensive, considering the problem from the perspective of a static game, and it needs an optimal strategy that dynamically reflects the mining pool for multiple games. Therefore, to solve the above problems, this paper uses the method of the evolutionary game to design a time-varying dynamic game model through the degree of system supervision and punishment. Based on establishing the game model, we use the method of replicating dynamic equations to analyze and find the optimal strategy for mining pool profits under different BWH attacks. The experimental results demonstrate that the mining pools will choose honest mining for the best profit over time under severe punishment and high supervision. On the contrary, if the blockchain system is supervised with a low penalty, the mining pools will eventually choose to launch BWH attacks against each other to obtain the optimal mining reward. These experimental results also prove the validity and correctness of our model and solution.

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Section: Blockchain Systemmentioning

confidence: 99%

Section: Blockchain Systemmentioning

confidence: 99%

An Evolutionary Game Theory-Based Method to Mitigate Block Withholding Attack in Blockchain System

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Various consensus models have been proposed for multi-agent systems in literature [1], [2]. Given the scope of consensus, it can be divided into centralized and decentralized consensus [3]. In the swarm robots with obstacles, the decentralized consensus in the robotic swarm is formed by integrating the opinions of neighbors, rather than referring to the opinions of all individuals in the swarm.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Consensus in Robotic Swarm for Collective Collision and Avoidance

FengYing,

Din,

HuiChao

et al. 2024

IEEE Access

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In this paper, a decentralized consensus algorithm for a robotic swarm is presented, which enable agents to escape collisions and avoid obstacles collectively. To achieve consensus, we have used neighboring clusters and extreme function for data dissemination and consensus in agents to avoid obstacles respectively. The main challenge is to make fast and accurate collective decision by improving data propagation in presence of Byzantine agents. To improve data dissemination in local interactions of agents in decentralized fashion, an iterative rumor-based data propagation model is proposed. Due to presence of Byzantine robots, the LCP and the W-MSR algorithm cannot achieve consensus for obstacle avoidance in artificial swarms. We establish the Expectation-based Extreme Value (EEV) algorithm using the local expectation and the extreme function to solve these problems. The experiments conducted in simulations demonstrate that the rumor spreading method has better results than the Peer-to-Peer method in randomly connected swarm signaling network (SSN) with complex environmental circumstance, the EEV algorithm is more effective than the LCP and the W-MSR for the swarm navigation and consensus in agent on large obstacles / environmental features. Furthermore, in presence of malicious / hacked agents in a swarm it is very difficult to reach consensus. The result show that proposed algorithm can handle the Byzantine agents effectively.

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“…Recent progresses in vision or laser-based localization and mapping, along with the increase in embedded computational power, have led to the development of mobile and aerial robots of reduced dimensions allowing larger swarms of robotic vehicles to effectively undertake such missions under realistic environmental and communication conditions. Nevertheless, interaction with humans and obstacles or the practical limitations of inter-vehicle communication data links still pose serious challenges that need to be consistently addressed for on-field deployment of teams of autonomous robots [1,2]. This requires the synthesis of distributed control algorithms with increased capabilities in terms of autonomy, safety and resilience.…”

Section: Introductionmentioning

confidence: 99%

Distributed Control for Multi-Robot Interactive Swarming Using Voronoi Partioning

Eudes,

Bertrand,

Marzat

et al. 2023

Drones

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The problem of safe navigation of a human-multi-robot system is addressed in this paper. More precisely, we propose a novel distributed algorithm to control a swarm of unmanned ground robots interacting with human operators in presence of obstacles. Contrary to many existing algorithms that consider formation control, the proposed approach results in non-rigid motion for the swarm, which more easily enables interactions with human operators and navigation in cluttered environments. Each vehicle calculates distributively and dynamically its own safety zone in which it generates a reference point to be tracked. The algorithm relies on purely geometric reasoning through the use of Voronoi partitioning and collision cones, which allows to naturally account for inter-robot, human-robot and robot-obstacle interactions. Different interaction modes have been defined from this common basis to address the following practical problems: autonomous waypoint navigation, velocity-guided motion, and follow a localized operator. The effectiveness of the algorithm is illustrated by outdoor and indoor field experiments.

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On the robustness of consensus-based behaviors for robot swarms

Cited by 9 publications

References 46 publications

An Evolutionary Game Theory-Based Method to Mitigate Block Withholding Attack in Blockchain System

An Evolutionary Game Theory-Based Method to Mitigate Block Withholding Attack in Blockchain System

Decentralized Consensus in Robotic Swarm for Collective Collision and Avoidance

Distributed Control for Multi-Robot Interactive Swarming Using Voronoi Partioning

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