CAREER: Crowdsourcing for Multirobot Coordination

职业：多机器人协调的众包

• 批准号: 1553726
• 负责人: Nora Ayanian
• 金额: $ 52.5万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553726&HistoricalAwards=false
• 关键词: CAREER; Crowdsourcing; Multirobot; Coordination

Teams of humans are exceptionally good at coordination. Teams of robots, however, are extremely clumsy at coordination, requiring extensive communication and computation. Reliance on this infrastructure poses a significant roadblock to bringing robot teams into real-world applications. This project is pursuing an integrated research, education, and outreach approach for developing novel, data-driven algorithms for multi-robot coordination, inspired by human coordination. As cognitive beings that make decisions based on broad context, memory, and sensing, human capabilities are challenging to transfer to robotics. To facilitate this transfer, the project is developing an online crowdsourcing application that tasks participants with creating a global structure, such as a shape. The application constrains participants to robot-like capabilities by limiting available information and actions. The application will provide a faithful representation of the capabilities of distributed teams of robots, and will be used to gain insights into human coordination that can then be transferred to a multi-robot system.The overarching goal of the proposed work is to develop novel methodologies for multi robot coordination firmly grounded in human collaboration, based on models learned from data collected via a crowdsourced online application. To this end, the research objectives are (1) to explicate the relationship between context (communication and sensing) and outcomes in distributed teams of humans working on tightly coupled tasks using data generated from an online multi-person interface; (2) to identify, using statistical methods, parameters for distributed teams of robots solving similar shared objective problems; (3) to infer, using deep learning architectures, diverse ensembles of coordination models for distributed teams of robots solving tightly coupled problems using the data collected from the crowdsourcing application; and (4) to validate these models by evaluating their success in solving tightly coupled problems using a combination of simulation, hardware, and mixed reality experiments.

人类团队非常善于协调。然而，机器人团队在协调方面极其笨拙，需要大量的通信和计算。对这种基础设施的依赖为将机器人团队带入现实世界的应用程序带来了重大障碍。该项目正在寻求一种综合的研究、教育和推广方法，以开发新颖的、数据驱动的多机器人协调算法，其灵感来自于人类协调。作为基于广泛的背景、记忆和感知做出决策的认知生物，人类的能力很难转移到机器人技术上。为了促进这种转移，该项目正在开发一个在线众包应用程序，该应用程序要求参与者创建一个全局结构，例如形状。该应用程序通过限制可用信息和操作来限制参与者具有类似机器人的功能。该应用程序将忠实地体现分布式机器人团队的能力，并将用于深入了解人类协调，然后将其转移到多机器人系统。拟议工作的总体目标是开发新颖的方法基于从众包在线应用程序收集的数据中学习到的模型，以人类协作为基础的多机器人协调。为此，研究目标是（1）使用在线多人界面生成的数据来解释分布式人类团队中的环境（通信和感知）和结果之间的关系，这些团队致力于紧密耦合的任务； (2) 使用统计方法识别解决类似共享目标问题的分布式机器人团队的参数； (3) 使用深度学习架构，推断分布式机器人团队的协调模型的不同集合，使用从众包应用程序收集的数据解决紧密耦合的问题； (4) 通过结合模拟、硬件和混合现实实验来评估这些模型在解决紧密耦合问题方面的成功程度来验证这些模型。

项目成果

DART: Diversity-enhanced Autonomy in Robot Teams

• DOI: 10.1177/0278364919839137
• 发表时间: 2019-03
• 期刊: The International Journal of Robotics Research
• 作者: Nora Ayanian

MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings

MAPFAST：使用最短路径嵌入进行多智能体路径查找的深度算法选择器

• 发表时间: 2021
• 期刊: Autonomous agents and multiagent systems
• 作者: Ren, Jingyao;Sathiyanarayanan, Vikraman;Ewing, Eric;Senbaslar, Baskin;Ayanian, Nora
• 通讯作者: Ayanian, Nora

Crowdsourced coordination through online games

通过网络游戏进行众包协调

• DOI: 10.1109/hri.2016.7451839
• 发表时间: 2016
• 期刊: 11th ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Tavakoli, Arash;Nalbandian, Haig;Ayanian, Nora
• 通讯作者: Ayanian, Nora

Betweenness Centrality in Multi-Agent Path Finding

多智能体路径查找中的介数中心性

• 发表时间: 2022
• 期刊: International Conference on Autonomous Agents and Multiagent Systems
• 作者: Eric Ewing;Jingyao Ren;Dhvani Kansara;Vikraman Sathiyanarayanan;Nora Ayanian
• 通讯作者: Nora Ayanian