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of 22
pro vyhledávání: '"Dagan, Ofer"'
Heterogeneous Bayesian decentralized data fusion captures the set of problems in which two robots must combine two probability density functions over non-equal, but overlapping sets of random variables. In the context of multi-robot dynamic systems,
Externí odkaz:
http://arxiv.org/abs/2401.16301
This paper considers the problem of evaluating an autonomous system's competency in performing a task, particularly when working in dynamic and uncertain environments. The inherent opacity of machine learning models, from the perspective of the user,
Externí odkaz:
http://arxiv.org/abs/2312.09033
A key challenge in Bayesian decentralized data fusion is the `rumor propagation' or `double counting' phenomenon, where previously sent data circulates back to its sender. It is often addressed by approximate methods like covariance intersection (CI)
Externí odkaz:
http://arxiv.org/abs/2307.10594
Autor:
Dagan, Ofer, Cinquini, Tycho L., Morrissey, Luke, Such, Kristen, Ahmed, Nisar R., Heckman, Christoffer
In many robotics problems, there is a significant gain in collaborative information sharing between multiple robots, for exploration, search and rescue, tracking multiple targets, or mapping large environments. One of the key implicit assumptions whe
Externí odkaz:
http://arxiv.org/abs/2306.04570
The factor graph decentralized data fusion (FG-DDF) framework was developed for the analysis and exploitation of conditional independence in {heterogeneous Bayesian decentralized fusion problems, in which robots update and fuse pdfs over different, b
Externí odkaz:
http://arxiv.org/abs/2209.08401
Autor:
Dagan, Ofer, Ahmed, Nisar R.
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
This paper presents a method for Bayesian multi-robot peer-to-peer data fusion where any pair of autonomous robots hold non-identical, but overlapping parts of a global joint probability distribution, representing real world inference tasks (e.g., ma
Externí odkaz:
http://arxiv.org/abs/2203.07142
Autor:
Dagan, Ofer, Ahmed, Nisar R.
Publikováno v:
021 IEEE 24th International Conference on Information Fusion (FUSION), Sun City, South Africa, 2021
This paper explores the use of factor graphs as an inference and analysis tool for Bayesian peer-to-peer decentralized data fusion. We propose a framework by which agents can each use local factor graphs to represent relevant partitions of a complex
Externí odkaz:
http://arxiv.org/abs/2106.13285
Autor:
Dagan, Ofer, Ahmed, Nisar R.
Publikováno v:
IEEE Transactions on Robotics 2023
In Bayesian peer-to-peer decentralized data fusion, the underlying distributions held locally by autonomous agents are frequently assumed to be over the same set of variables (homogeneous). This requires each agent to process and communicate the full
Externí odkaz:
http://arxiv.org/abs/2101.11116
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