Occupancy grid based distributed MPC for mobile robots

2017 ◽  
Author(s):  
Mohamed W. Mehrez ◽  
Tobias Sprodowski ◽  
Karl Worthmann ◽  
George K.I. Mann ◽  
Raymond G. Gosine ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Occupancy Grid ◽  
Grid Based

Dynamic world modeling for a mobile robot among moving objects

Robotica ◽  
1996 ◽  
Vol 14 (5) ◽  
pp. 553-560
Author(s):  
Yuefeng Zhang ◽  
Robert E. Webber
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Moving Objects ◽  
Grid Method ◽  
Experimental Results ◽  
Sensor Data ◽  
Probabilistic Estimation ◽  
Occupancy Grid ◽  
World Modeling ◽  
Grid Based

SUMMARYA grid-based method for detecting moving objects is presented. This method involves the extension and combination of two methods: (1) the Hough Transform and (2) the Occupancy Grid method. The Occupancy Grid method forms the basis for a probabilistic estimation of the location and velocity of objects in the scene from the sensor data. The Hough Transform enables the new method to handle non-integer velocity values. A model for simulating a sonar ring is also presented. Experimental results show that this method can handle objects moving at non-integer velocities.

Detection and Tracking of Moving Obstacles (DATMO): A Review

Robotica ◽  
2019 ◽  
Vol 38 (5) ◽  
pp. 761-774 ◽  
Author(s):  
Ángel Llamazares ◽  
Eduardo J. Molinos ◽  
Manuel Ocaña
Keyword(s):  
Mobile Robots ◽  
State Of The Art ◽  
Indoor Environments ◽  
Local Planning ◽  
Planning Stage ◽  
Moving Obstacles ◽  
Model Free ◽  
Detection And Tracking ◽  
Free Model ◽  
Grid Based

SummaryWorking with mobile robots, prior to execute the local planning stage, they must know the environment where they are moving. For that reason the perception and mapping stages must be performed previously. This paper presents a survey in the state of the art in detection and tracking of moving obstacles (DATMO). The aim of what follows is to provide an overview of the most remarkable methods at each field specially in indoor environments where dynamic obstacles can be potentially more dangerous and unpredictable. We are going to show related DATMO methods organized in three approaches: model-free, model-based and grid-based. In addition, a comparison between them and conclusions will be presented.

A Probability Occupancy Grid Based Approach for Real-Time LiDAR Ground Segmentation

2020 ◽  
Vol 21 (3) ◽  
pp. 998-1010 ◽  
Author(s):  
Zhongzhen Luo ◽  
Martin V. Mohrenschildt ◽  
Saeid Habibi
Keyword(s):  
Real Time ◽  
Occupancy Grid ◽  
Ground Segmentation ◽  
Grid Based

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.

scholarly journals Dedicated Filter for Robust Occupancy Grid Mapping

2014 ◽  
Vol 4 (1) ◽  
pp. 82
Author(s):  
KS Nagla ◽  
Moin Uddin ◽  
Dilbag Singh
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Internal Representation ◽  
Optimal Solution ◽  
Autonomous Mobile Robots ◽  
Infrared Sensors ◽  
Occupancy Grid ◽  
Frame Work ◽  
High Level ◽  
Fundamental Requirement

<p>Sensor based perception of the environment is an emerging area of the mobile robot research where sensors play a pivotal role. For autonomous mobile robots, the fundamental requirement is the convergent of the range information in to high level internal representation. Internal representation in the form of occupancy grid is commonly used in autonomous mobile robots due to its various advantages. There are several sensors such as vision sensor, laser rage finder, and ultrasonic and infrared sensors etc. play roles in mapping. However the sensor information failure, sensor inaccuracies, noise, and slow response are the major causes of an error in the mapping. To improve the reliability of the mobile robot mapping multisensory data fusion is considered as an optimal solution. This paper presents a novel architecture of sensor fusion frame work in which a dedicated filter (DF) is proposed to increase the robustness of the occupancy grid for indoor environment. The technique has been experimentally verified for different indoor test environments. The proposed configuration shows improvement in the occupancy grid with the implementation of dedicated filters.</p>

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