Robot position estimation using range and compass data

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

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.

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