CAREER: Crowdsourcing for Multirobot Coordination

职业：多机器人协调的众包

基本信息

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

项目摘要

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.

人类团队擅长协调。但是，机器人团队在协调方面非常笨拙，需要广泛的沟通和计算。依赖这种基础设施为将机器人团队带入现实世界应用程序带来了重大的障碍。该项目正在追求一种综合研究，教育和外展方法，以开发出受其协调启发的新型，数据驱动的多机器人协调算法。作为基于广泛背景，记忆和感知决定的决策的认知生物，人类的能力在转移到机器人技术方面具有挑战性。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，该应用程序任务参与者创建一个全球结构，例如形状。该应用程序通过限制可用信息和动作来限制参与者的类似机器人的功能。 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.为此，研究目标是（1）使用从在线多人界面生成的数据来阐明上下文（交流和传感）之间的关系以及分布式人类的成果，从事紧密耦合任务的关系；（2）使用统计方法来识别机器人分布式团队的参数，以解决相似的共享目标问题；（3）为了使用深度学习体系结构推断，使用从众包应用程序中收集的数据来解决分布式机器人的分布式机器人团队的各种协调模型；（4）通过使用仿真，硬件和混合现实实验的组合来评估它们在解决紧密耦合问题方面的成功来验证这些模型。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
作者：
Nora Ayanian
通讯作者：
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

DOI：
发表时间：
2021
期刊：
Autonomous agents and multiagent systems
影响因子：
0
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
影响因子：
0
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

DOI：
发表时间：
2022
期刊：
International Conference on Autonomous Agents and Multiagent Systems
影响因子：
0
作者：
Eric Ewing;Jingyao Ren;Dhvani Kansara;Vikraman Sathiyanarayanan;Nora Ayanian
通讯作者：
Nora Ayanian