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.

Robot navigation in dynamic environments using global-appearance descriptors. State of the art

2015 ◽  
Vol 2 (1) ◽  
pp. 2
Author(s):  
Yerai Berenguer Fernández
Keyword(s):  
Mobile Robots ◽  
Visual Information ◽  
State Of The Art ◽  
Robot Navigation ◽  
Continuous Optimization ◽  
Dynamic Environments ◽  
Autonomous Mobile Robots ◽  
Map Building ◽  
Future Directions ◽  
Robot Position

Map building and localization are two impor- tant abilities that autonomous mobile robots must develop. This way, much research has been carried out on these topics, and researchers have proposed many approaches to address these problems. This work presents a state of the art report on map building and localization using global appearance descriptors. In this approach, robots capture visual information from the environment and obtain, usually by means of a transformation, a global appearance descriptor for each image. Using these descriptors, the robot is able to estimate its location in a map previously built, which is also composed of a set of global appearance descriptors. Several previous investigations that have led to the approach of this research are summarized in this paper, such as researches that compare several methods of creating global appearance descriptors. In these works we observe how the continuous optimization of the algorithms has lead to better estimations of the robot position within the environment. Finally a number of future directions in which researches are currently working are listed. 

Optimization of granulation for fuzzy controllers of autonomous mobile robots using the Firefly Algorithm

2018 ◽  
Vol 4 (2) ◽  
pp. 185-195 ◽  
Author(s):  
Marylu L. Lagunes ◽  
Oscar Castillo ◽  
Jose Soria ◽  
Mario Garcia ◽  
Fevrier Valdez
Keyword(s):  
Mobile Robots ◽  
Firefly Algorithm ◽  
Autonomous Mobile Robots ◽  
Fuzzy Controllers

scholarly journals An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
M. Hentschel ◽  
B. Wagner
Keyword(s):  
Mobile Robots ◽  
Indoor Environment ◽  
Human Memory ◽  
Autonomous Mobile Robots ◽  
Memory Model ◽  
Adaptive Memory ◽  
Variant Information ◽  
Environmental Representation ◽  
Over Time

This paper introduces an environmental representation for autonomous mobile robots that continuously adapts over time. The presented approach is inspired by human memory information processing and stores the current as well as past knowledge of the environment. In this paper, the memory model is applied to time-variant information about obstacles and driveable routes in the workspace of the autonomous robot and used for solving the navigation cycle of the robot. This includes localization and path planning as well as vehicle control. The presented approach is evaluated in a real-world experiment within changing indoor environment. The results show that the environmental representation is stable, improves its quality over time, and adapts to changes.

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.

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