Distributed Joint Localization and Tracking for Multi-robot Networks Under Local Sensing and Communication Constraints with Theoretical Guarantees

具有理论保证的局部感知和通信约束下的多机器人网络分布式联合定位与跟踪

• 批准号: 2027139
• 负责人: Wei Ren
• 金额: $ 49.18万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027139&HistoricalAwards=false
• 关键词: Distributed; Joint; Localization; Tracking; Multi

This Dynamics, Control, and System Diagnostics project will support research to enable large-scale, distributed coordination of multi-robot systems, promoting the progress of science and advancing the national prosperity. In applications such as surveillance, security, and disaster response, a network of mobile autonomous robots collectively tracks a subject over a wide area. As the subject and the robots move in and out of observation and communication range, each robot in the network needs to propagate estimates of position, velocity, and orientation for both itself and the subject, based on noisy measurements. The first task is called "localization," and the second task is called "tracking." Communication limitations mean that it is often infeasible to rely on a central controller, so each robot must be responsible for making its own decisions. This project will address the problem of how to coordinate information among the individual robots, to allow each of them to make the best possible decisions. Results from the project will benefit numerous applications involving multi-robot systems such as surveillance and security, disaster response and environmental monitoring. Education and outreach activities will broaden participation of underrepresented groups in research. The objective of this project is to address the fundamental theoretical and practical issues caused by sensing gaps, local communication, and lack of absolute pose measurements in distributed collaborative sensing with mobile robotic networks. The first thrust is to derive a unified framework for fully distributed joint localization and target tracking through local cooperation. The second thrust is rigorous theoretical analysis of the framework with realistic issues, namely, sparse and changing sensing and communication and uncertain target models. The third thrust is experimental demonstration on mobile robotic networks with physical constraints. The research will significantly advance distributed estimation in both theory and applications.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.

这个动力学，控制和系统诊断项目将支持研究，使多机器人系统的大规模，分布式协调，促进科学的进步和促进国家的繁荣。在诸如监视、安全和灾难响应等应用中，移动的自主机器人网络在广阔的区域内集体跟踪对象。 当主体和机器人移入和移出观察和通信范围时，网络中的每个机器人需要基于噪声测量来传播其自身和主体的位置、速度和方向的估计。第一个任务称为“定位”，第二个任务称为“跟踪”。“通信限制意味着依赖中央控制器通常是不可行的，因此每个机器人必须负责做出自己的决定。该项目将解决如何协调各个机器人之间的信息的问题，使每个机器人都能做出最好的决策。该项目的结果将有利于涉及多机器人系统的许多应用，如监控和安全，灾难响应和环境监测。教育和外联活动将扩大代表性不足的群体对研究的参与。 该项目的目标是解决基本的理论和实际问题所造成的传感差距，本地通信，并缺乏绝对的姿态测量与移动的机器人网络的分布式协作传感。第一个推力是通过局部协作得到一个完全分布式联合定位和目标跟踪的统一框架。第二个推力是严格的理论分析的框架与现实的问题，即，稀疏和不断变化的传感和通信和不确定的目标模型。第三个推力是实验演示移动的机器人网络与物理约束。该研究将在理论和应用方面显著推进分布式估计。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

Distributed Kalman Filter for 3-D Moving Object Tracking over Sensor Networks

• DOI: 10.1109/cdc42340.2020.9303740
• 发表时间: 2020-12
• 期刊: 2020 59th IEEE Conference on Decision and Control (CDC)
• 作者: Pengxiang Zhu;W. Ren

Multirobot Fully Distributed Active Joint Localization and Target Tracking

多机器人全分布式主动关节定位和目标跟踪

• DOI: 10.1109/tcst.2023.3240525
• 发表时间: 2023
• 期刊: IEEE Transactions on Control Systems Technology
• 影响因子: 4.8
• 作者: Su, Shaoshu;Zhu, Pengxiang;Ren, Wei
• 通讯作者: Ren, Wei

PL-CVIO: Point-Line Cooperative Visual-Inertial Odometry

• DOI: 10.1109/ccta54093.2023.10253266
• 发表时间: 2023-08
• 期刊: 2023 IEEE Conference on Control Technology and Applications (CCTA)
• 作者: Yanyu Zhang;Pengxiang Zhu;Wei Ren

Distributed Invariant Extended Kalman Filter for 3-D Dynamic State Estimation Using Lie Groups

• DOI: 10.23919/acc53348.2022.9867483
• 发表时间: 2022-06
• 期刊: 2022 American Control Conference (ACC)
• 作者: J. Xu;Pengxiang Zhu;W. Ren

An Optimization Approach to Fully Distributed Active Joint Localization and Target Tracking in Multi-Robot Systems

多机器人系统中全分布式主动联合定位和目标跟踪的优化方法

• DOI: 10.23919/acc53348.2022.9867337
• 发表时间: 2022
• 期刊: American Control Conference
• 作者: Su, Shaoshu;Zhu, Pengxiang;Ren, Wei
• 通讯作者: Ren, Wei