Prototyping of Kinematics Simulator for Supporting Autonomous Mobile Robot Development

2016 ◽  
Vol 28 (4) ◽  
pp. 470-478 ◽  
Author(s):  
Kitaro Shimane ◽  
◽  
Ryo Ueda ◽  
Susumu Tarao
Keyword(s):  
Mobile Robot ◽  
Robot Control ◽  
Autonomous Mobile Robot ◽  
Motion Model ◽  
Simulation Experiments ◽  
Occupancy Grid ◽  
Sensor Model ◽  
Environment Model ◽  
Geometric Relationship ◽  
Basic Configuration

[abstFig src='/00280004/05.jpg' width='300' text='Appearance of the kinematics simulator' ] A kinematics simulator for an autonomous mobile robot has been proposed to simulate complicated motions such as those caused by the interaction between a robot and its environment in terms of geometric relationship. The simulator is expected to assist in the development of a robot control system for autonomous running in the real world. This paper presents the simulator concept, its basic configuration, and the results of preliminary simulation experiments, which have been performed to evaluate a simple motion model, and an environment model based on occupancy grid maps and a laser range finder pseudo sensor model consisting of a typical probabilistic density. The results of the simulation experiments using the aforementioned multiple models are also presented to demonstrate that the simulator can perform in various numerical environments.

Development of Autonomous Mobile Robot “MML-05” Based on i-Cart Mini for Tsukuba Challenge 2015

2016 ◽  
Vol 28 (4) ◽  
pp. 461-469 ◽  
Author(s):  
Tomoyoshi Eda ◽  
◽  
Tadahiro Hasegawa ◽  
Shingo Nakamura ◽  
Shin’ichi Yuta
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
The Self ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Localization Method ◽  
Occupancy Grid ◽  
Grid Map ◽  
Local Grid ◽  
Occupancy Grid Map

[abstFig src='/00280004/04.jpg' width='300' text='Autonomous mobile robots entered in the Tsukuba Challenge 2015' ] This paper describes a self-localization method for autonomous mobile robots entered in the Tsukuba Challenge 2015. One of the important issues in autonomous mobile robots is accurately estimating self-localization. An occupancy grid map, created manually before self-localization has typically been utilized to estimate the self-localization of autonomous mobile robots. However, it is difficult to create an accurate map of complex courses. We created an occupancy grid map combining local grid maps built using a leaser range finder (LRF) and wheel odometry. In addition, the self-localization of a mobile robot was calculated by integrating self-localization estimated by a map and matching it to wheel odometry information. The experimental results in the final run of the Tsukuba Challenge 2015 showed that the mobile robot traveled autonomously until the 600 m point of the course, where the occupancy grid map ended.

A New Cockroach Swarm Optimization for Motion Planning of Mobile Robot

2012 ◽  
Vol 263-266 ◽  
pp. 834-838
Author(s):  
Wanhui Liu ◽  
Le Cheng
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Euclidean Distance ◽  
Optimal Path ◽  
Autonomous Mobile Robot ◽  
Weight Factor ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Heuristic Information

In this paper, an improved cockroach swarm optimization, called cockroach swarm optimization with expansion gird (CSO-EG), is presented and applied to motion planning of autonomous mobile robot. In CSO-EG, the expansion gird method is used to model workspace. By computing the weight factor, the Euclidean distance from each candidate to the destination cell and the pheromone strength of each candidate cell are use as the heuristic information together. For increasing the variety of path, a random choosing cell strategy is introduced. The simulation experiments demonstrate that the CSO-EG algorithm can quickly get the optimal or near-optimal path in a workspace populated with obstacles.

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