Collaborative Research: Coordinating Robot Teams Using Market-Based Mechanisms

协作研究：利用基于市场的机制协调机器人团队

• 批准号: 0412912
• 负责人: Pinar Keskinocak
• 金额: $ 15.4万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-01 至 2010-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0412912&HistoricalAwards=false
• 关键词: Collaborative; Research; Coordinating; Robot; Teams

Collaborative Research: Coordinating Robot Teams using Market-Based MechanismsIn the near future, teams of robots will take on tasks in initially unknown environments where human presence is not possible due to safety or cost reasons, including applications where robots have to visit or cover areas in initially unknown terrain, such as mine sweeping and de-mining, search and rescue operations after earthquakes, the exploration of distant planets, and hazardous material cleaning. The efficient and robust coordination of the robots is imperative for all of these scenarios. This project develops and implements methods for the dynamic assignment and re-assignment of tasks to robot teams through the use of combinatorial auctions in the context of exploration tasks in initially unknown environments. Previous work has demonstrated the use of single-item auctions for multi-robot task allocation, in which robots bid on tasks that are auctioned off one at a time. Unfortunately, single-item auctions do not take synergies between tasks into account, which can result in suboptimal task allocations and poor team performance. This project studies more complex auctions, including combinatorial auctions, where robots bid on bundles of tasks. Initial feasibility studies show that combinatorial auctions generally lead to significantly superior team performance compared to single-item auctions, and generate very good results compared to optimal centralized methods. The project focuses on combinatorial bidding strategies which result in team behavior that is efficient, effective, adaptable to dynamic environments, and robust in the presence of error conditions. It also studies alternative winner determination methods for various objective functions, such as minimizing the total travel distance (or energy consumption) and the total time to task completion. This interdisciplinary project between robotics and auction researchers will produce both theoretical and experimental results, in particular, new methods with theoretical analyses of their correctness and efficiency and their demonstration on both simple and complex multi-robot exploration scenarios with real-time requirements. The students involved in this research will learn to work as part of an interdisciplinary team and will deepen their understanding of two usually separate areas of research.

协作研究：使用以市场为基础的机制协调机器人团队在不久的将来，机器人团队将在最初未知的环境中执行任务，在这些环境中，由于安全或成本原因，人类无法在场，包括机器人必须访问或覆盖最初未知地形中的区域的应用，如扫雷和扫雷、地震后的搜索和救援行动、遥远行星的探索以及危险材料的清理。在所有这些情况下，机器人的高效和强大的协调是必不可少的。该项目开发和实施了通过使用组合拍卖，在最初未知环境中的探索任务的背景下，向机器人团队动态分配和重新分配任务的方法。以前的工作已经证明了将单项目拍卖用于多机器人任务分配，即机器人对一次拍卖一个任务的投标。不幸的是，单一物品拍卖没有考虑到任务之间的协同效应，这可能导致次优的任务分配和糟糕的团队绩效。这个项目研究更复杂的拍卖，包括组合拍卖，在组合拍卖中，机器人对一捆捆的任务出价。初步可行性研究表明，与单一物品拍卖相比，组合拍卖通常会产生显著优越的团队绩效，并且与最优集中拍卖方法相比，会产生非常好的结果。该项目专注于组合竞标策略，这些策略导致团队行为高效、有效、适应动态环境，并在存在错误条件时具有健壮性。它还研究了各种目标函数的替代赢家确定方法，如最小化总行程(或能量消耗)和任务完成的总时间。这个机器人学和拍卖研究人员之间的跨学科项目将产生理论和实验结果，特别是新方法，对其正确性和效率进行理论分析，并在简单和复杂的具有实时要求的多机器人探索场景中进行演示。参与这项研究的学生将学习如何作为跨学科团队的一部分工作，并将加深他们对两个通常独立的研究领域的理解。