scholarly journals Smart Navigation for an In-pipe Robot Through Multi-phase Motion Control and Particle Filtering Method

2021 ◽  
Author(s):  
Saber Kazeminasab ◽  
Vahid Janfaza ◽  
Moein Razavi ◽  
M. Katherine Banks
Keyword(s):  
Motion Control ◽  
Particle Filtering ◽  
Filtering Method ◽  
Multi Phase ◽  
Phase Motion

scholarly journals Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1090
Author(s):  
Wenxu Wang ◽  
Damián Marelli ◽  
Minyue Fu
Keyword(s):  
Maximum Likelihood ◽  
Particle Filtering ◽  
Indoor Localization ◽  
Real Data ◽  
High Accuracy ◽  
Classical Particle ◽  
Popular Approach ◽  
Filtering Method ◽  
Essential Idea ◽  
Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

Kinematic synthesis of adjustable moving pivot four-bar mechanisms for multi-phase motion generation

1996 ◽  
Vol 31 (4) ◽  
pp. 459-474 ◽  
Author(s):  
Shao Jie Wang ◽  
Raj S. Sodhi
Keyword(s):  
Motion Generation ◽  
Kinematic Synthesis ◽  
Multi Phase ◽  
Phase Motion

scholarly journals Convergence of particle filtering method for nonlinear estimation of vortex dynamics

2010 ◽  
Vol 4 (3) ◽  
Author(s):  
Sivaguru S Sritharan ◽  
Meng Xu
Keyword(s):  
Vortex Dynamics ◽  
Particle Filtering ◽  
Nonlinear Estimation ◽  
Filtering Method

scholarly journals A Bayesian particle filtering method for brain source localisation

2015 ◽  
Vol 47 ◽  
pp. 192-204 ◽  
Author(s):  
Xi Chen ◽  
Simo Särkkä ◽  
Simon Godsill
Keyword(s):  
Particle Filtering ◽  
Filtering Method ◽  
Source Localisation

scholarly journals 4D pine scale: biomimetic 4D printed autonomous scale and flap structures capable of multi-phase movement

2020 ◽  
Vol 378 (2167) ◽  
pp. 20190445 ◽  
Author(s):  
David Correa ◽  
Simon Poppinga ◽  
Max D. Mylo ◽  
Anna S. Westermeier ◽  
Bernd Bruchmann ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Image Correlation ◽  
Technical Structure ◽  
Cellulose Fibrils ◽  
Longitudinal Bending ◽  
Scale Structures ◽  
Multi Phase ◽  
Printing Techniques ◽  
Shape Changes ◽  
Phase Motion

We developed biomimetic hygro-responsive composite polymer scales inspired by the reversible shape-changes of Bhutan pine ( Pinus wallichiana ) cone seed scales. The synthetic kinematic response is made possible through novel four-dimensional (4D) printing techniques with anisotropic material use, namely copolymers with embedded cellulose fibrils and ABS polymer. Multi-phase motion like the subsequent transversal and longitudinal bending deformation during desiccation of a natural pinecone scale can be structurally programmed into such printed hygromorphs. Both the natural concept generator (Bhutan pinecone scale) and the biomimetic technical structure (4D printed scale) were comparatively investigated as to their displacement and strain over time via three-dimensional digital image correlation methods. Our bioinspired prototypes can be the basis for tailored autonomous and self-sufficient flap and scale structures performing complex consecutive motions for technical applications, e.g. in architecture and soft robotics. This article is part of the theme issue ‘Bioinspired materials and surfaces for green science and technology (part 3)’.

scholarly journals An enhanced particle filtering method for GMTI radar tracking

2016 ◽  
Vol 52 (3) ◽  
pp. 1408-1420 ◽  
Author(s):  
Miao Yu ◽  
Cunjia Liu ◽  
Baibing Li ◽  
Wen-Hua Chen
Keyword(s):  
Particle Filtering ◽  
Radar Tracking ◽  
Filtering Method
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