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NRI: FND: COLLAB: Distributed, Semantically-Aware Tracking and Planning for Fleets of Robots

NRI：FND：COLLAB：机器人舰队的分布式语义感知跟踪和规划

基本信息

批准号：
1830419
负责人：
Philip Dames
金额：
$ 28.14万
依托单位：
Temple University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830419&HistoricalAwards=false
关键词：
NRI FND COLLAB Distributed Semantically

项目摘要

The ability to track, predict and reason about pedestrians and vehicles in a fast-paced dense urban environment is crucial to ensuring that autonomous vehicles can operate safely and dependably. This project focuses on providing that capability to fleets of autonomous cars and delivery drones, allowing these autonomous systems to realize their promised societal benefits, such as the potential for greater mobility of people and goods while reducing traffic congestion and increasing safety. This technology can moreover be customized for other applications such as large manufacturing operations and even small household robotic applications. The methods and results from this project will be included in course curricula and in used outreach programs and events.The project approaches this challenge in several ways: (i) combine classification algorithms from machine vision with the motion tracking of the objects from multi-target Bayesian filters into a new filtering architecture; (ii) generate new online, distributed tessellation algorithms, using tools from Voronoi-based distributed coverage control, to dynamically partition the surrounding environment in a way that leverages the innate parallelism of teams of multi-robots; (iii) use this partition to create a distributed memory architecture that has bandwidth-efficient updates and ensures data integrity in the face of system errors and malicious agents; (iv) develop semantically-aware path planning algorithms for fast, online optimization of robot motion that account for the range of possible reactionary behaviors of other objects; and (v) design an app-based interface that facilitates two-way information exchange between a human operator and the multi-robot team. A prototype system based on these advances will be tested and evaluated in a highly instrumented laboratory environment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在快节奏的密集城市环境中跟踪、预测和推理行人和车辆的能力，对于确保自动驾驶车辆能够安全可靠地运行至关重要。该项目的重点是为自动汽车和无人机交付车队提供这种能力，使这些自动系统能够实现其承诺的社会效益，例如提高人员和货物流动性的潜力，同时减少交通拥堵并提高安全性。此外，该技术还可以针对其他应用进行定制，例如大型制造业务，甚至小型家用机器人应用。该项目的方法和结果将被纳入课程和使用的推广计划和活动中。该项目以几种方式应对这一挑战：（i）将机器视觉的分类算法与多目标贝叶斯过滤器的对象运动跟踪结合到一个新的过滤架构中;（ii）使用来自基于Voronoi的分布式覆盖控制的工具来生成新的在线分布式曲面细分算法，以利用多机器人团队的固有并行性的方式动态地划分周围环境;（iii）使用该分区来创建分布式存储器架构，该架构具有带宽高效更新并且在面对系统错误和恶意代理时确保数据完整性;（iv）开发语义感知的路径规划算法，用于快速、在线优化机器人运动，该算法考虑到其他对象的可能反应行为的范围;以及（v）设计基于应用程序的界面，该界面促进人类操作员和多机器人团队之间的双向信息交换。基于这些进步的原型系统将在高度仪器化的实验室环境中进行测试和评估。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Collision-Free Distributed Multi-Target Tracking Using Teams of Mobile Robot with Localization Uncertainty

具有定位不确定性的移动机器人团队的无碰撞分布式多目标跟踪

DOI：
发表时间：
2020
期刊：
Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems
影响因子：
0
作者：
Chen, Jun;Dames, Philip
通讯作者：
Dames, Philip

Distributed Multi-Target Search and Tracking Using a Coordinated Team of Ground and Aerial Robots

使用地面和空中机器人协调团队进行分布式多目标搜索和跟踪

DOI：
发表时间：
2020
期刊：
Robotics science and systems
影响因子：
0
作者：
Chen, Jun;Dames, Philip
通讯作者：
Dames, Philip

Multi-class Target Tracking Using the Semantic PHD Filter

DOI：
10.1007/978-3-030-95459-8_32
发表时间：
2019
期刊：
2015 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
0
作者：
Jun Chen-;P. Dames
通讯作者：
Jun Chen-;P. Dames

The semantic PHD filter for multi-class target tracking: From theory to practice

DOI：
10.1016/j.robot.2021.103947
发表时间：
2021-12
期刊：
Robotics Auton. Syst.
影响因子：
0
作者：
Jun Chen;Zhanteng Xie;P. Dames
通讯作者：
Jun Chen;Zhanteng Xie;P. Dames

Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 [Competitions]

高度受限空间中的自主地面导航：从 ICRA 2022 基准自主机器人导航挑战赛中汲取的经验教训 [竞赛]

DOI：
10.1109/mra.2022.3213466
发表时间：
2022
期刊：
IEEE Robotics & Automation Magazine
影响因子：
5.7
作者：
Xiao, Xuesu;Xu, Zifan;Wang, Zizhao;Song, Yunlong;Warnell, Garrett;Stone, Peter;Zhang, Tingnan;Ravi, Shravan;Wang, Gary;Karnan, Haresh
通讯作者：
Karnan, Haresh