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.

scholarly journals Real‐time keypoints detection for autonomous recovery of the unmanned ground vehicle

2020 ◽  
Vol 14 (17) ◽  
pp. 4690-4700
Author(s):  
Jie Li ◽  
Sheng Zhang ◽  
Kai Han ◽  
Xia Yuan ◽  
Chunxia Zhao ◽  
...  
Keyword(s):  
Real Time ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Autonomous Recovery ◽  
Keypoints Detection

Motion and Energy Efficiency Parameters of the Unmanned Ground Vehicle

2015 ◽  
Vol 220-221 ◽  
pp. 934-939 ◽  
Author(s):  
Eero Väljaots ◽  
Raivo Sell ◽  
Mati Kaeeli
Keyword(s):  
Energy Efficiency ◽  
Data Acquisition ◽  
Real Time ◽  
Design Process ◽  
Field Tests ◽  
Unmanned Ground Vehicles ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Acquisition Method

The paper investigates the data acquisition method and a system of wheeled mobile unmanned ground vehicles (UGV) for characterization and optimization of motion and energy efficiency. This enables to conduct real-time and conditional field tests. The obtained results are used for an advanced methodology framework for robotic design targeted on the development, simulation and testing of vehicle platforms along the entire design process.

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