A novel lidar-driven two-level approach for real-time unmanned ground vehicle navigation and map building

2014 ◽  
Author(s):  
Chaomin Luo ◽  
Mohan Krishnan ◽  
Mark Paulik ◽  
Bo Cui ◽  
Xingzhong Zhang
Keyword(s):  
Real Time ◽  
Vehicle Navigation ◽  
Map Building ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle

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

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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