A grey probability measure set based mobile robot position estimation algorithm

2015 ◽  
Vol 13 (4) ◽  
pp. 978-985 ◽  
Author(s):  
Peng Wang ◽  
Qi-Bin Zhang ◽  
Zong-Hai Chen
Keyword(s):  
Probability Measure ◽  
Mobile Robot ◽  
Estimation Algorithm ◽  
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

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

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

A position estimation system for mobile robots using a monocular image of a 3-D landmark

Robotica ◽  
1994 ◽  
Vol 12 (5) ◽  
pp. 431-441 ◽  
Author(s):  
Kyoung C. Koh ◽  
Jae S. Kim ◽  
Hyung S. Cho
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Camera System ◽  
System A ◽  
The Neural Network ◽  
Monocular Image ◽  
Estimation System ◽  
Learning Data

SUMMARYThis paper presents an absolute position estimation system for a mobile robot moving on a flat surface. In this system, a 3-D landmark with four coplanar points and a non-coplanar point is utilized to improve the accuracy of position estimation and to guide the robot during navigation. Applying theoretical analysis, we investigate the image sensitivity of the proposed 3-D landmark compared with the conventional 2-D landmark. In the camera calibration stage of the experiments, we employ a neural network as a computational tool. The neural network is trained from a set of learning data collected at various points around the mark so that the extrinsic and intrinsic parameters of the camera system can be resolved. The overall estimation algorithm from the mark identification to the position determination is implemented in a 32-bit personal computer with an image digitizer and an arithmetic accelerator. To demonstrate the effectiveness of the proposed 3-D landmark and the neural network-based calibration scheme, a series of navigation experiments were performed on a wheeled mobile robot (LCAR) in an indoor environment. The results show the feasibility of the position estimation system applicable to mobile robot's real-time navigation.

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