Continuity properties of the appearance manifold for mobile robot position estimation

2001 ◽  
Vol 19 (11) ◽  
pp. 741-752 ◽  
Author(s):  
J.L Crowley ◽  
F Pourraz
Keyword(s):  
Mobile Robot ◽  
Position Estimation ◽  
Continuity Properties ◽  
Robot Position

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 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.

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

Investigation of ROS Based Environment Modelling and Mobile Robot Position Estimation with Dead Reckoning and Uncertainties

2021 ◽  
Author(s):  
Hamzah Ahmad ◽  
Mohammad Heerwan Peeie ◽  
Mohd Syakirin Ramli ◽  
Amir Akramin Bin Shafie ◽  
Mohd Hezri Fazalul Rahiman
Keyword(s):  
Mobile Robot ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Robot Position ◽  
Environment Modelling

scholarly journals Mobile robot position estimation using Euler-Maruyama algorithm

2019 ◽  
Vol 610 ◽  
pp. 012074
Author(s):  
Mohamed G Abd Elfatah ◽  
Hany Nasry Zaky ◽  
M Gharib
Keyword(s):  
Mobile Robot ◽  
Position Estimation ◽  
Robot Position
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