Stereovision-based real-time obstacle detection scheme for Unmanned Ground Vehicle with steering wheel drive mechanism

2017 ◽  
Author(s):  
Maham Khan ◽  
Saad Hassan ◽  
Syed Irfan Ahmed ◽  
Jamshed Iqbal
Keyword(s):  
Real Time ◽  
Obstacle Detection ◽  
Steering Wheel ◽  
Unmanned Ground Vehicle ◽  
Detection Scheme ◽  
Ground Vehicle ◽  
Drive Mechanism ◽  
Wheel Drive

Real time prediction and diagnostics for unmanned ground vehicle (UGV) mobility

2007 ◽  
Author(s):  
Holger M. Jaenisch ◽  
James W. Handley ◽  
Michael L. Hicklen
Keyword(s):  
Real Time ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Time Prediction

A Virtual Unmanned Ground Vehicle Simulator for Verifying the Algorithm based on Real-time Traversability Analysis

2018 ◽  
Vol 24 (4) ◽  
pp. 354-360
Author(s):  
Hajun Song ◽  
Jong-Boo Han ◽  
Hyosung Hong ◽  
Samuel Jung ◽  
Sung-Soo Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Vehicle Simulator

59225 THE DEVELOPMENT OF A REAL-TIME SIMULATOR FOR UNMANNED GROUND VEHICLE(Control of Multibody Systems)

2010 ◽  
Vol 2010.5 (0) ◽  
pp. _59225-1_-_59225-8_
Author(s):  
Jong Seok Lee ◽  
Jae Yi Oh ◽  
Yeo Giel Yoon ◽  
Ju Yong Kang ◽  
Won Gun Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Multibody Systems ◽  
Vehicle Control ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle

scholarly journals Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Feng Ding ◽  
Yibing Zhao ◽  
Lie Guo ◽  
Mingheng Zhang ◽  
Linhui Li
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Line Segment ◽  
Obstacle Detection ◽  
Corner Detection ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Markov Random ◽  
Cross Country ◽  
Hybrid Cross

In order to detect the obstacle from the large amount of 3D LIDAR data in hybrid cross-country environment for unmanned ground vehicle, a new graph approach based on Markov random field was presented. Firstly, the preprocessing method based on the maximum blurred line is applied to segment the projection of every laser scan line inx-yplane. Then, based onK-means clustering algorithm, the same properties of the line are combined. Secondly, line segment nodes are precisely positioned by using corner detection method, and the next step is to take advantage of line segment nodes to build an undirected graph for Markov random field. Lastly, the energy function is calculated by means of analyzing line segment features and solved by graph cut. Two types of line mark are finally classified into two categories: ground and obstacle. Experiments prove the feasibility of the approach and show that it has better performance and runs in real time.

Nonlinear Model Predictive Controller for an Unmanned Ground Vehicle on Variable Terrain

2015 ◽  
Author(s):  
Andrew Eick ◽  
David Bevly
Keyword(s):  
Real Time ◽  
Nonlinear Model ◽  
Control Algorithm ◽  
Target Location ◽  
Tire Model ◽  
Unmanned Ground Vehicle ◽  
Model Predictive Controller ◽  
Ground Vehicle ◽  
Rollover Risk ◽  
Predictive Controller

Rough, off-road terrain contains multiple hazards for an unmanned ground vehicle (UGV). In this paper, hazards are classified into three groups: obstacles, rough traversable terrain, and rough untraversable terrain. These three types of hazards create a rollover risk for a UGV. A nonlinear model predictive controller (NMPC) that is capable of navigating a UGV through these hazards is presented. The control algorithm features a nonlinear tire model which more accurately captures the dynamics of the UGV when compared to a linearized tire model, and has a fast enough run time for real time implementation. On an actual vehicle, the UGV is assumed to be equipped with a perception based sensor, such as a Light Detection And Ranging (LiDAR) unit, to provide information of the terrain roughness, grade, and elevation. This information is used by the NMPC to safely control the vehicle to a target location. However, for the purposes of this paper, control inputs and terrain are simulated in Car-Sim [1], and the feasibility of real time implementation is investigated.

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