Consensus-based fast and energy-efficient multi-robot task allocation

Mahato, Prabhat; Saha, Sudipta; Sarkar, Chayan; Shaghil, Md.

doi:10.1016/j.robot.2022.104270

Cited by 16 publications

(2 citation statements)

References 54 publications

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“…Consequently, distributed consensus solutions have been developed [27,28]. Distributed consensus algorithms greatly reduce the demands on individual perception capabilities, the communication bandwidth, and computational resources, while offering benefits such as cost-effectiveness, robustness, adaptability, scalability, and stealthiness [22,29,30].…”

Section: Related Workmentioning

confidence: 99%

Advancing Convergence Speed of Distributed Consensus Time Synchronization Algorithms in Unmanned Aerial Vehicle Ad Hoc Networks

Wu,

Bai,

2024

Drones

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Time synchronization is a critical prerequisite for unmanned aerial vehicle ad hoc networks (UANETs) to facilitate navigation and positioning, formation control, and data fusion. However, given the dynamic changes in UANETs, improving the convergence speeds of distributed consensus time synchronization algorithms with only local information poses a major challenge. To address this challenge, this study first establishes a convex model on the basis of graph theory and relevant theories of random matrices to approximate the original problem. Subsequently, three acceleration schemes for consensus algorithms are derived by minimizing the Frobenius norm of the iteration matrix. Additionally, this study provides a new upper bound for constant communication weights and discusses the limitations of existing metrics used to measure the convergence speeds of consensus algorithms. Finally, the proposed schemes are compared with existing ones through simulation. Our results indicate that the three proposed schemes can achieve faster convergence while maintaining high-precision synchronization in scenarios with static or known topological structures of networks. In scenarios where the topological structure of a UANET is time-varying and unknown, the scheme proposed in this paper achieves the fastest convergence speed.

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Section: Related Workmentioning

confidence: 99%

Advancing Convergence Speed of Distributed Consensus Time Synchronization Algorithms in Unmanned Aerial Vehicle Ad Hoc Networks

Wu,

Bai,

2024

Drones

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“…However, these methods depend mainly on the centralized agent where it can result in delays, inefficiencies when this agent is not available or not well-informed [25], [26]. Methods of task allocation based on decentralized approaches [27], [28] relies on making decisions at the level of each robot [29], [30], where these robots are responsible to choose tasks to perform based on their criteria [31], [32]. Therefore, these methods are more resilient because they are not based on a centralized agent for decision-making [33], [34].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks

Msala,

Hamed,

Talea

et al. 2023

Journal of Robotics and Control

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This paper presents an innovative task allocation method for multi-robot systems that aims to optimize task distribution while taking into account various performance metrics such as efficiency, speed, and cost. Contrary to conventional approaches, the proposed method takes a comprehensive approach to initialization by integrating the K-means clustering algorithm, the Hungarian method for solving the assignment problem, and a genetic algorithm specifically adapted for Open Loop Travel Sales Man Problem (OLTSP). This synergistic combination allows for a more robust initialization, effectively grouping similar tasks and robots, and laying a strong foundation for the subsequent optimization process. The suggested method is flexible enough to handle a variety of situations, including Multi-Robot System (MRS) with robots that have unique capabilities and tasks of varying difficulty. The method provides a more adaptable and flexible solution than traditional algorithms, which might not be able to adequately address these variations because of the heterogeneity of the robots and the complexity of the tasks. Additionally, ensuring optimal task allocation is a key component of the suggested method. The method efficiently determines the best task assignments for robots through the use of a systematic optimization approach, thereby reducing the overall cost and time needed to complete all tasks. This contrasts with some existing methods that might not ensure optimality or might have limitations in their ability to handle a variety of scenarios. Extensive simulation experiments and numerical evaluations are carried out to validate the method's efficiency. The extensive validation process verifies the suggested approach's dependability and efficiency, giving confidence in its practical applicability.

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A Two-Hop Neighborhood Based Berserk Detection Algorithm for Probabilistic Model of Consensus in Distributed Ledger Systems

Mitra,

Cortesi,

Chaki

2023

Lecture Notes in Computer Science

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Consensus-based fast and energy-efficient multi-robot task allocation

Cited by 16 publications

References 54 publications

Advancing Convergence Speed of Distributed Consensus Time Synchronization Algorithms in Unmanned Aerial Vehicle Ad Hoc Networks

Advancing Convergence Speed of Distributed Consensus Time Synchronization Algorithms in Unmanned Aerial Vehicle Ad Hoc Networks

A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks

A Two-Hop Neighborhood Based Berserk Detection Algorithm for Probabilistic Model of Consensus in Distributed Ledger Systems

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