The sensor selection task of the Gaussians mixture Bayes' with regularised EM (GMB-REM) technique in robot position estimation

2003 ◽  
Author(s):  
T. Koshizen
Keyword(s):  
Position Estimation ◽  
Sensor Selection ◽  
Selection Task ◽  
Robot Position

scholarly journals CONCURRENT MAP BUILDING AND LOCALIZATION ON INDOOR DYNAMIC ENVIRONMENTS

2002 ◽  
Vol 16 (03) ◽  
pp. 361-374 ◽  
Author(s):  
JUAN ANDRADE-CETTO ◽  
ALBERTO SANFELIU
Keyword(s):  
Learning Rule ◽  
Dynamic Environments ◽  
Position Estimation ◽  
Indoor Environments ◽  
Map Building ◽  
Robust Learning ◽  
Map Construction ◽  
Map Updating ◽  
Dynamic Map ◽  
Robot Position

A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and Kalman filtering for map updating and robot position estimation allows for robust learning of moderately dynamic indoor environments.

Robot position estimation using range and compass data

1992 ◽  
Author(s):  
Raashid Malik ◽  
Braham Benteftifa
Keyword(s):  
Position Estimation ◽  
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
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