2A2-I06 Mobile Robot Position Estimation Using Mirror Sheets Located in Environments(Localization and Mapping(2))

2012 ◽  
Vol 2012 (0) ◽  
pp. _2A2-I06_1-_2A2-I06_2
Author(s):  
Kohsei MATSUMOTO ◽  
Shuichi MAKI ◽  
Ryoso MASAKI ◽  
Motoya TANIGUCHI
Keyword(s):  
Mobile Robot ◽  
Position Estimation ◽  
Localization And Mapping ◽  
Robot Position

scholarly journals Simultaneous Localization and Mapping of Mobile Robot Based on Improved RBPF

2018 ◽  
Vol 160 ◽  
pp. 06002
Author(s):  
Jinging Zhang ◽  
Xiaogang Ruan ◽  
Pengfei Dong ◽  
Jing Zhou
Keyword(s):  
Mobile Robot ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Bacterial Foraging Optimization ◽  
The Real ◽  
Bacterial Foraging ◽  
Operation Speed ◽  
Localization And Mapping ◽  
Bacterial Foraging Algorithm ◽  
Robot Position

The traditional SLAM based on RBPF has the problem of constructing high-precision map which requires large amounts of particles to make the calculation complexity and the phenomenon of particle depletion caused by particle degradation. Aiming at these problems, an improved RBPF particle filter based on adaptive bacterial foraging optimization algorithm and adaptive resampling is proposed for mobile robot SLAM problem. Firstly, the introduction of adaptive bacterial foraging algorithm to RBPF making the distribution of particles before resampling closer to the real situation. Then use the adaptive resampling method makes the newly generated particles closer to the real movement, thereby increasing the robot position estimation accuracy and map creation accuracy. The experimental results show that this method can improve the practicability of the system, reduce the computational complexity, improve the operation speed and get more effective particles while guaranteeing the accuracy of the grid map.

scholarly journals State Estimation for Mobile Robot using Partially Observable Markov Decision Process

1996 ◽  
Vol 8 (3) ◽  
pp. 272-277
Author(s):  
Daehee Kang ◽  
◽  
Hideki Hashimoto ◽  
Fumio Harashima
Keyword(s):  
Mobile Robot ◽  
Markov Decision Process ◽  
Decision Process ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Estimation Errors ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
Partially Observable ◽  
Robot Position

Dead Reckoning has been commonly used for position estimation. However, this method has inherent problems, one of the biggest being it always cumulates estimation errors. In this paper, we propose a new method to estimate a current mobile robot state using Partially Observable Markov Decision Process (POMDP). POMDP generalizes the Markov Decision Process (MDP) framework to the case where the agent must make its decisions in partial ignorance of its current situation. Here, the robot state means the robot position or current subgoal at which the mobile robot is located. It is shown that we will be able to estimate the mobile robot state precisely and robustly, even if the environment is changed slightly, through a case study.

scholarly journals Mobile Robot Position Estimation using Milstein Algorithm

2021 ◽  
Vol 1970 (1) ◽  
pp. 012005
Author(s):  
Mohamed G Abd Elfatah ◽  
Hany Nasry Zaky ◽  
Ahmed Shams
Keyword(s):  
Mobile Robot ◽  
Position Estimation ◽  
Robot Position

scholarly journals Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

2021 ◽  
Vol 2129 (1) ◽  
pp. 012018
Author(s):  
R J Musridho ◽  
H Hasan ◽  
H Haron ◽  
D Gusman ◽  
M A Mohammad
Keyword(s):  
Mobile Robots ◽  
Convergence Analysis ◽  
Firefly Algorithm ◽  
Estimation Error ◽  
Position Estimation ◽  
Autonomous Mobile Robots ◽  
Localization And Mapping ◽  
Robot Position ◽  
Modified Firefly Algorithm ◽  
Over Time

Abstract In autonomous mobile robots, Simultaneous Localization and Mapping (SLAM) is a demanding and vital topic. One of two primary solutions of SLAM problem is FastSLAM. In terms of accuracy and convergence, FastSLAM is known to degenerate over time. Previous work has hybridized FastSLAM with a modified Firefly Algorithm (FA), called unranked Firefly Algorithm (uFA), to optimize the accuracy and convergence of the robot and landmarks position estimation. However, it has not shown the performance of the accuracy and convergence. Therefore, this work is done to present both mentioned performances of FastSLAM and uFA-FastSLAM to see which one is better. The result of the experiment shows that uFA-FastSLAM has successfully improved the accuracy (in other words, reduced estimation error) and the convergence consistency of FastSLAM. The proposed uFA-FastSLAM is superior compared to conventional FastSLAM in estimation of landmarks position and robot position with 3.30 percent and 7.83 percent in terms of accuracy model respectively. Furthermore, the proposed uFA-FastSLAM also exhibits better performances compared to FastSLAM in terms of convergence consistency by 93.49 percent and 94.20 percent for estimation of landmarks position and robot position respectively.

A Method of Mobile Robot Position Estimation Correction Using Visual Location of Natural Landmarks

2017 ◽  
Vol 18 (11) ◽  
pp. 752-758
Author(s):  
D. N. Stepanov ◽  
◽  
E. Yu. Smirnova ◽  
Keyword(s):  
Mobile Robot ◽  
Position Estimation ◽  
Robot Position

Model-based vision system for mobile robot position estimation

1994 ◽  
Author(s):  
Tiziana D'Orazio ◽  
Liborio Capozzo ◽  
Massimo Ianigro ◽  
Arcangelo Distante
Keyword(s):  
Mobile Robot ◽  
Vision System ◽  
Position Estimation ◽  
Model Based ◽  
Robot Position
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