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    Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization

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    Type
    Article
    Authors
    Jaleel, Hassan
    Shamma, Jeff S. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    RISC Laboratory
    Date
    2020-10-27
    Online Publication Date
    2020-10-27
    Print Publication Date
    2020-11
    Submitted Date
    2019-11-22
    Permanent link to this record
    http://hdl.handle.net/10754/665668
    
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    Abstract
    Recent advances in sensing, communication, and computing technologies have enabled the use of multirobot systems for practical applications such as surveillance, area mapping, and search and rescue. For such systems, a major challenge is to design decision rules that are real-time-implementable, require local information only, and guarantee some desired global performance. Distributed optimization provides a framework for designing such local decision-making rules for multirobot systems. In this article, we present a collection of selected results for distributed optimization for robot networks. We will focus on two special classes of problems: 1) real-time path planning for multirobot systems and 2) self-organization in multirobot systems using game-theoretic approaches. For multirobot path planning, we will present some recent approaches that are based on approximately solving distributed optimization problems over continuous and discrete domains of actions. The main idea underlying these approaches is that a variety of path planning problems can be formulated as convex optimization and submodular minimization problems over continuous and discrete action spaces, respectively. To generate local update rules that are efficiently implementable in real time, these approaches rely on approximate solutions to the global problems that can still guarantee some level of desired global performance. For game-theoretic self-organization, we will present a sampling of results for area coverage and real-time target assignment. In these results, the problems are formulated as games, and online updating rules are designed to enable teams of robots to achieve the collective objective in a distributed manner.
    Citation
    Jaleel, H., & Shamma, J. S. (2020). Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization. Proceedings of the IEEE, 108(11), 1953–1967. doi:10.1109/jproc.2020.3028295
    Publisher
    IEEE
    Journal
    Proceedings of the IEEE
    DOI
    10.1109/JPROC.2020.3028295
    Additional Links
    https://ieeexplore.ieee.org/document/9241495/
    https://ieeexplore.ieee.org/document/9241495/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9241495
    ae974a485f413a2113503eed53cd6c53
    10.1109/JPROC.2020.3028295
    Scopus Count
    Collections
    Articles; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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