Multi-Robot Task Allocation Based on Swarm Intelligence

In book: Multi-Robot Systems, Trends and Development
Source: InTech
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    • "In a centralized protocol, decisions on robot task allocations are made by a single entity (e.g., a coordinator robot or the base station) once all the relevant information has been collected. Data flow from every entity (robot) to the coordinator, which runs a centralized algorithm (e.g., [5]) and notifies each robot on its set of assigned tasks. A distributed algorithm, on the other hand, bears a much higher degree of autonomy, since multiple entities are involved in deciding on an appropriate response. "
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    ABSTRACT: Wireless sensor and robot networks (WSRNs) have emerged as a paradigmatic class of cyber-physical systems with cooperating objects. Due to the robots’ potential to unleash a wider set of networking means and thus augment the network performance, WSRNs have rapidly become a hot research area. In this article, we elaborate on WSRNs from two unique standpoints: robot task allocation and robot task fulfillment. The former deals with robots cooperatively deciding on the set of tasks to be individually carried out to achieve a desired goal; the latter enables robots to fulfill the assigned tasks through intelligent mobility scheduling.
    IEEE Communications Magazine 07/2012; 50(7):47-54. DOI:10.1109/MCOM.2012.6231291 · 4.01 Impact Factor
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    ABSTRACT: Wireless body area sensing networks have the potential to revolutionize health care in the near term. The coupling of biosensors with a wireless infrastructure enables the real-time monitoring of an individual¿s health and related behaviors continuously, as well as the provision of realtime feedback with nimble, adaptive, and personalized interventions. The KNOWME platform is reviewed, and lessons learned from system integration, optimization, and in-field deployment are provided. KNOWME is an endto- end body area sensing system that integrates off-the-shelf sensors with a Nokia N95 mobile phone to continuously monitor and analyze the biometric signals of a subject. KNOWME development by an interdisciplinary team and in-laboratory, as well as in-field deployment studies, employing pediatric obesity as a case study condition to monitor and evaluate physical activity, have revealed four major challenges: (1) achieving robustness to highly varying operating environments due to subject-induced variability such as mobility or sensor placement, (2) balancing the tension between acquiring high fidelity data and minimizing network energy consumption, (3) enabling accurate physical activity detection using a modest number of sensors, and (4) designing WBANs to determine physiological quantities of interest such as energy expenditure. The KNOWME platform described in this article directly addresses these challenges.
    IEEE Communications Magazine 05/2012; 50(5):116-125. DOI:10.1109/MCOM.2012.6194391 · 4.01 Impact Factor
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    ABSTRACT: A method of task allocation and automated negotiation for multi robots was proposed. Firstly, the principles of task allocation were described based on the real capability of robot. Secondly, the model of automated negotiation was constructed, in which Least-Squares Support Vector Regression (LSSVR) was improved to estimate the opponent’s negotiation utility and the robust controller of H ∞ output feedback was employed to optimize the utility performance indicators. Thirdly, the protocol of negotiation and reallocation was proposed to improve the real-time capability and task allocation. Finally, the validity of method was proved through experiments.
    Journal of Electronics (China) 11/2012; 29(6). DOI:10.1007/s11767-012-0868-x


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