Collaborative Research: Coordinating Robot Teams Using Market-Based Mechanisms

合作研究：在不久的将来，机器人团队将在最初未知的环境中承担任务，其中人类由于安全或成本原因而无法存在，包括机器人必须访问或覆盖最初未知地形的区域的应用，例如扫雷和排雷，地震后的搜索和救援行动，遥远星球的探索，有害物质清理。对于所有这些场景，机器人的高效和强大的协调是必不可少的。该项目开发和实施的动态分配和重新分配任务的机器人团队，通过使用组合拍卖的背景下，在最初未知的环境中的探索任务的方法。以前的工作已经证明了使用单项目拍卖多机器人任务分配，其中机器人投标的任务，拍卖了一次一个。不幸的是，单一物品拍卖没有考虑任务之间的协同作用，这可能导致次优的任务分配和糟糕的团队绩效。该项目研究更复杂的拍卖，包括组合拍卖，其中机器人对任务进行投标。初步的可行性研究表明，组合拍卖通常会导致显着上级团队绩效相比，单一项目拍卖，并产生非常好的结果相比，最佳的集中式方法。该项目的重点是组合投标策略，导致团队的行为是高效，有效的，适应动态环境，并在错误的情况下存在鲁棒性。它还研究了各种目标函数的替代赢家确定方法，例如最小化总行程距离（或能量消耗）和任务完成的总时间。这个机器人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，特别是新方法的正确性和效率的理论分析，以及对具有实时要求的简单和复杂多机器人探索场景的演示。参与这项研究的学生将学习作为跨学科团队的一部分工作，并加深他们对两个通常独立的研究领域的理解。

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.