Navigation Based on Metric Route Information in Places Where the Mobile Robot Visits for the First Time

2019 ◽  
Vol 31 (2) ◽  
pp. 180-193
Author(s):  
Asahi Handa ◽  
◽  
Azumi Suzuki ◽  
Hisashi Date ◽  
Ryohsuke Mitsudome ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Navigation System ◽  
Geometric Accuracy ◽  
Localization And Mapping ◽  
Route Information ◽  
Set Up ◽  
The University ◽  
Local Map ◽  
First Time

In this study, we propose a navigation system that guides a robot at a location visited for the first time, without developing a map in advance. First, it estimates the position of a path that exists on the local map by matching the metric route information and the local map generated by simultaneous localization and mapping (SLAM); this is achieved by using a particle filter. Then, the robot travels to the destination along the estimated route. In this system, the geometric accuracy of the route information specified in advance and the accuracy of the map generated by SLAM are essential. Furthermore, it is necessary to recognize the traversable area. The experiment performed verifies the matching of the route information and local map. In the autonomous running experiment, we conduct a trial run on a course set up at the University of Tsukuba.

scholarly journals A LAB TASK GROUP FOR REVIEW AND CONTINUOUS IMPROVEMENT OF THIRD AND FOURTH-YEAR LABORATORY COURSES

2021 ◽  
Author(s):  
David Torvi ◽  
Scott Noble ◽  
Doug Bitner ◽  
Melanie Fauchoux ◽  
Rob Peace ◽  
...  
Keyword(s):  
Continuous Improvement ◽  
Lessons Learned ◽  
Task Group ◽  
Laboratory Courses ◽  
Use Of Data ◽  
Hands On ◽  
Remote Labs ◽  
Set Up ◽  
The University ◽  
First Time

Since the mid-1980’s, the mechanical engineering program at the University of Saskatchewan has included three core third and fourth-year lab courses, each of which consists of 9-10 individual labs. In 2015 a task group was set up to review these courses, including deliverables, scheduling and links to material in corecourses. Since this time, the task group has taken on the major responsibility for continuous improvement of the lab program, including reviewing student evaluations, making changes to labs, and recommending equipment purchases.  The task group has also been responsible for a major redesign of the lab program, which will improve delivery and scheduling of labs, alignment with core courses, workload of students, and experience gained by graduate teaching assistants. Smaller apparatus have been designed and built in-house to allow students to gain additional hands-on experience. Labs have been designed to build on one another in order to systematically improve students’ general laboratory skills, including the use of data acquisition systems and experimental design. This new approach was used for the first time in ME 328 in 2019-20.  This paper will focus primarily on the role of the task group in continuous improvement, and the lab program redesign.  The new ME 328 course is described, along with lessons learned from the first offering. The task group’s role in moving to remote labs during COVID-19 is also discussed.

Congratulations! Journal of Robotics and Mechatronics Best Paper Award 2020

2021 ◽  
Vol 33 (1) ◽  
pp. 1-2
Author(s):  
Keyword(s):  
Mobile Robot ◽  
Selection Process ◽  
Paper Award ◽  
Award Winner ◽  
Editorial Committee ◽  
Award Ceremony ◽  
The Future ◽  
Route Information ◽  
First Time

Navigation Based on Metric Route Information in Places Where the Mobile Robot Visits for the First Time We are pleased to announce that the 13th Journal of Robotics and Mechatronics Best Paper Award (JRM Best Paper Award 2020) has been decided by the JRM editorial committee. The following paper won the JRM Best Paper Award 2020, severely selected from among all 77 papers published in Vol.31 (2019). The Best Paper Award ceremony was held in Gakushi-Kaikan, Tokyo, Japan, on December 23, 2020 (both on-site and online), attended by the authors and JRM editorial committee members who took part in the selection process. The award winner will also be announced on the JRM website and was given a certificate and a nearly US$1,000 honorarium. We congratulate the winners and sincerely wish them success in the future. Asahi Handa, Azumi Suzuki, Hisashi Date, Ryohsuke Mitsudome, Takashi Tsubouchi, and Akihisa Ohya J. Robot. Mechatron., Vol.31, No.2, pp. 180-193, April 2019 doi: 10.20965/jrm.2019.p0180

Omnidirectional Vision Based Mobile Robot Hierarchical SLAM

2011 ◽  
Vol 464 ◽  
pp. 95-98
Author(s):  
Mao Hai Li ◽  
Li Ning Sun ◽  
Ming Qiang Pan
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Position Estimation ◽  
Medium Size ◽  
Accurate Estimation ◽  
Omnidirectional Vision ◽  
Alignment Algorithm ◽  
Feature Maps ◽  
Topological Graph ◽  
Local Map

A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps of large environments is proposed. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF). The particle filter is used to extend the path posterior by sampling new poses that integrate the current observation which drastically reduces the uncertainty about the robot pose. The landmark position estimation and update is also implemented through Kalman filter. Omnidirectional vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching Scale Invariant Feature Transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-Tree. Experimental results on real robot in a medium size, real indoor environment show the practicality and efficiency of our proposed method.

scholarly journals Implementation and Reconfiguration of Robot Operating System on Human Follower Transporter Robot

2015 ◽  
Vol 9 (2) ◽  
pp. 59
Author(s):  
Addythia Saphala ◽  
Prianggada Indra Tanaya
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
Navigation System ◽  
Robot Navigation ◽  
Simultaneous Localization And Mapping ◽  
Kinect Sensor ◽  
Operation System ◽  
Robot Operating System ◽  
Localization And Mapping

Robotic Operation System (ROS) is an im- portant platform to develop robot applications. One area of applications is for development of a Human Follower Transporter Robot (HFTR), which  can  be  considered  as a custom mobile robot utilizing differential driver steering method and equipped with Kinect sensor. This study discusses the development of the robot navigation system by implementing Simultaneous Localization and Mapping (SLAM).

scholarly journals Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yong Dai ◽  
Ming Zhao
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Data Association ◽  
Simultaneous Localization And Mapping ◽  
Fast Convergence ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Local Data ◽  
Localization And Mapping ◽  
Indoor Mobile Robot

An artificial intelligent grey wolf optimizer (GWO)-assisted resampling scheme is applied to the Rao-Blackwellized particle filter (RBPF) in the simultaneous localization and mapping (SLAM). By doing this, we can make the diversity of the particles resampling and then obtain a better localization accuracy and fast convergence to realize indoor mobile robot SLAM. In addition, we propose an adaptive local data association (Range-SLAM) scheme to improve the computational efficiency for the algorithm of the nearest neighbor (NN) data association in the iteration of the RBPF prediction. Through the experiment and simulations, the proposed SLAM schemes have fast convergence, accuracy, and heuristics. Therefore, the improved RBPF and new data association schemes presented in this paper can provide a feasible method for the indoor mobile robot SLAM.

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