Multi-strategy based exploration for 3D mapping in unknown environments using a mobile robot

2019 ◽  
Author(s):  
Jie Ding ◽  
Yongchun Fang
Keyword(s):  
Mobile Robot ◽  
3D Mapping ◽  
Unknown Environments

A mobile robot based system for fully automated thermal 3D mapping

2014 ◽  
Vol 28 (4) ◽  
pp. 425-440 ◽  
Author(s):  
Dorit Borrmann ◽  
Andreas Nüchter ◽  
Marija Ðakulović ◽  
Ivan Maurović ◽  
Ivan Petrović ◽  
...  
Keyword(s):  
Mobile Robot ◽  
3D Mapping

scholarly journals Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775248 ◽  
Author(s):  
Tzu-Chao Lin ◽  
Chao-Chun Chen ◽  
Cheng-Jian Lin
Keyword(s):  
Mobile Robot ◽  
Differential Evolution ◽  
Fuzzy Controller ◽  
Dynamic Group ◽  
Unknown Environments ◽  
Navigation Control ◽  
Interval Type ◽  
Wall Following ◽  
Neural Fuzzy

This study developed and effectively implemented an efficient navigation control of a mobile robot in unknown environments. The proposed navigation control method consists of mode manager, wall-following mode, and towards-goal mode. The interval type-2 neural fuzzy controller optimized by the dynamic group differential evolution is exploited for reinforcement learning to develop an adaptive wall-following controller. The wall-following performance of the robot is evaluated by a proposed fitness function. The mode manager switches to the proper mode according to the relation between the mobile robot and the environment, and an escape mechanism is added to prevent the robot falling into the dead cycle. The experimental results of wall-following show that dynamic group differential evolution is superior to other methods. In addition, the navigation control results further show that the moving track of proposed model is better than other methods and it successfully completes the navigation control in unknown environments.

Adaptive Line Extraction for Slam Line Based Applications in Unknown Environments

2011 ◽  
Vol 403-408 ◽  
pp. 2057-2064
Keyword(s):  
Mobile Robot ◽  
Laser Scanner ◽  
Line Extraction ◽  
International Conference ◽  
Unknown Environments ◽  
Extraction Algorithm ◽  
Ieee International Conference

Paper has been removed due to plagiarism. The original was published in the Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, February 21 - 26, 2009. Recursive Line Extraction Algorithm from 2D Laser Scanner Applied to Navigation a Mobile Robot, Mohammad Norouzi, Mostafa Yaghobi, Mohammad Rezai Siboni, Mahdi Jadaliha

scholarly journals Developing a real time navigation for the mobile robots at unknown environments

2020 ◽  
Vol 20 (1) ◽  
pp. 500
Author(s):  
Sarah Haider Abdulredah ◽  
Dheyaa Jasim Kadhim
Keyword(s):  
Mobile Robot ◽  
Mean Square ◽  
Matlab Simulation ◽  
Robot Operating System ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Software Platforms ◽  
The Mean ◽  
Simulation Results ◽  
Robot Platform

<p><span>This research deals with the feasibility of a mobile robot to navigate and discover its location at unknown environments, and then constructing maps of these navigated environments for future usage. In this work, we proposed a modified Extended Kalman Filter- Simultaneous Localization and Mapping (EKF-SLAM) technique which was implemented for different unknown environments containing a different number of landmarks. Then, the detectable landmarks will play an important role in controlling the overall navigation process and EKF-SLAM technique’s performance. MATLAB simulation results of the EKF-SLAM technique come with better performance as compared with an odometry approach performance in terms of measuring the mean square error, especially when increasing the number of landmarks. After that, we simulate and evaluate a mobile robot platform named TurtleBot2e in Gazebo simulator software to achieve the using of the SLAM technique for a different environment using the Rviz library which was built on Robot Operating System in Linux. The main conclusion comes with this work is the simulation and implementation of the SLAM technique using two software platforms separately (MATLAB and ROS) in different unknown environments containing a different number of landmarks so a few number of landmark will make the mobile robot loses its path.</span></p>

scholarly journals Robust 2D Mapping Integrating with 3D Information for the Autonomous Mobile Robot Under Dynamic Environment

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1503 ◽  
Author(s):  
Bin Zhang ◽  
Masahide Kaneko ◽  
Hun-ok Lim
Keyword(s):  
Mobile Robot ◽  
Moving Objects ◽  
Three Dimensional ◽  
Dynamic Environment ◽  
Mapping Method ◽  
Research Field ◽  
Working Environment ◽  
3D Mapping ◽  
Occupancy Probability ◽  
3D Information

In order to move around automatically, mobile robots usually need to recognize their working environment first. Simultaneous localization and mapping (SLAM) has become an important research field recently, by which the robot can generate a map while moving around. Both two-dimensional (2D) mapping and three-dimensional (3D) mapping methods have been developed greatly with high accuracy. However, 2D maps cannot reflect the space information of the environment and 3D mapping needs long processing time. Moreover, conventional SLAM methods based on grid maps take a long time to delete the moving objects from the map and are hard to delete the potential moving objects. In this paper, a 2D mapping method integrating with 3D information based on immobile area occupied grid maps is proposed. Objects in 3D space are recognized and their space information (e.g., shapes) and properties (moving objects or potential moving objects like people standing still) are projected to the 2D plane for updating the 2D map. By using the immobile area occupied grid map method, recognized still objects are reflected to the map quickly by updating the immobile area occupancy probability with a high coefficient. Meanwhile, recognized moving objects and potential moving objects are not used for updating the map. The unknown objects are reflected to the 2D map with a lower immobile area occupancy probability so that they can be deleted quickly once they are recognized as moving objects or start to move. The effectiveness of our method is proven by experiments of mapping under dynamic indoor environment using a mobile robot.

Sign in / Sign up

Export Citation Format

Share Document