Multi-agent Motion Tracking Using the Particle Filter in ISpace with DINDs

2006 ◽  
pp. 1063-1067
Author(s):  
TaeSeok Jin ◽  
ChangHoon Park ◽  
SooHong Park
Keyword(s):  
Particle Filter ◽  
Motion Tracking ◽  
Multi Agent

scholarly journals Co-Reading as a Generative Ontology

2018 ◽  
Vol 6 (3) ◽  
pp. 151-163
Author(s):  
Simon Biggs
Keyword(s):  
Dark Matter ◽  
Motion Tracking ◽  
Interactive Systems ◽  
Body Motion ◽  
Interactive System ◽  
Assemblage Theory ◽  
Multi Agent ◽  
Technical Considerations ◽  
Full Body

This paper discusses the immersive full body motion tracking installation Dark Matter, developed by the author and completed in early 2016. The paper outlines the conceptual focus of the project, including the use of the metaphor of dark matter to explore questions around interactive systems and assemblage. The primary technical considerations involved in the project are also outlined. ‘Co-reading' is proposed as a framework for a generative ontology, within the context of assemblage theory, deployed within a multimodal multi-agent interactive system.

scholarly journals Nonlinear Attitude and Formation Estimation Spacecraft And Multi-Agent Systems

2021 ◽  
Author(s):  
Michael Rososhansky
Keyword(s):  
Particle Filter ◽  
Nonlinear Filtering ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Multi Agent ◽  
Filtering Technique ◽  
Estimation And Control ◽  
And Control

This dissertation examines the state and parameter estimation problem of monolithic spacecraft and multi-agent systems in conjunction with the control algorithms. Nonlinear filtering techniques are investigated and applied to the problems of attitude estimation and control of monolithic spacecraft, distributed flltering for attitude estimation and control of satellite formation flying (SFF), and estimation and control of a multi-agent system in consensus tracking with uncertain dynamic model. The main objective is to investigate the performance of nonlinear filtering techniques under fault-free and fault-prone scenarios. In essence, the core of this research has been placed on identifying techniques to improve the efficiency and reduce the variance of estimations in nonlinear filtering. The research is primarily dedicated to the investigation of adaptive unscented Kalman Filter (AUKF) and particle Filter (PF). A nonlinear filtering technique has been proposed for sequential joint estimation of a multi-agent system in consensus tracking with uncertain dynamic model. The new filter is called marginalized unscented particle Filter (MUPF). The proposed filter uses the Rao-Blackwellised principle to couple the particle filtering technique with unscented transform algorithm

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