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TraderBots: A new paradigm for robust and efficient multirobot coordination in dynamic environments.

机译:TraderBots:动态环境中强大而高效的多机器人协调的新范例。

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The challenge of efficient multirobot coordination has risen to the forefront of robotics research in recent years. The wide range of applications demanding multirobot solutions motivates interest in this problem. In general, multirobot coordination strategies assume either a centralized approach, where a single agent plans for the group, or a distributed approach, where each robot is responsible for its own planning. Inherent to many centralized approaches are difficulties such as intractable solutions for large groups, sluggish response to changes in the local environment, heavy communication requirements, and brittle systems with single points of failure. The key advantage of centralized approaches is that they can produce globally optimal plans. While most distributed approaches can overcome these difficulties, they can only produce suboptimal plans because they cannot take full advantage of information available to all team members.; This work develops TraderBots, a market-based coordination approach that is inherently distributed, but can also opportunistically form centralized subgroups to improve efficiency. Robots are self-interested with the primary goal of maximizing individual profits. The revenue and cost models and the rules of engagement are designed so that maximizing individual profit has the benevolent effect of, on average, moving the team toward the globally optimal solution. This approach inherits the flexibility of markets in allowing cooperation and competition to emerge opportunistically. This dissertation addresses the multirobot coordination problem for autonomous robotic teams executing tasks in dynamic environments where it is efficient solutions are desirable. Contributions of this dissertation are the first extensive investigation of the application of market-based techniques to multirobot coordination, the most versatile coordination-approach for dynamic multirobot application domains, the first market-based approach to multirobot coordination that allows opportunistic optimization by "leaders", the first in-depth investigation of the requirements for robust multirobot coordination in dynamic environments, the most extensive implementation of a market-based approach to multirobot coordination, and first steps in a systematic approach for evaluating and comparing multirobot coordination strategies.
机译:近年来,有效的多机器人协调挑战已成为机器人研究的前沿。要求多机器人解决方案的广泛应用激发了对此问题的兴趣。通常,多机器人协作策略采用集中化方法(由单个代理为组计划)或分布式方法(由每个机器人负责自己的计划)。许多集中式方法固有的困难是,例如针对大型团体的棘手解决方案,对本地环境变化的反应迟钝,通信要求高以及具有单点故障的易碎系统。集中式方法的主要优势在于它们可以生成全局最佳计划。尽管大多数分布式方法都可以克服这些困难,但它们只能制定次优计划,因为它们不能充分利用所有团队成员可用的信息。这项工作开发了TraderBots,这是一种基于市场的协调方法,其固有地是分布式的,但也可以机会性地形成集中的子组以提高效率。机器人具有自身利益,其主要目标是使个人利益最大化。设计收入和成本模型以及参与规则,以使个人利润最大化具有平均的有益效果,平均而言,这将使团队朝着全球最优解决方案发展。这种方法继承了市场的灵活性,允许合作和竞争机会性地出现。本文针对需要在动态环境中执行有效任务的自主机器人团队解决多机器人协调问题。本论文的贡献是对基于市场的技术在多机器人协调中的应用进行的首次广泛研究,动态多机器人应用领域中最通用的协调方法,第一个基于市场的多机器人协调方法(允许“领导者”进行机会性优化) ,这是对动态环境中强大的多机器人协调要求的首次深入研究,是基于市场的多机器人协调方法最广泛的实施,并且是评估和比较多机器人协调策略的系统方法的第一步。

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