Virtual Testing and Policy Deployment Framework for Autonomous Navigation of an Unmanned Ground Vehicle Using Reinforcement Learning

2021 ◽  
Author(s):  
Tyrell Lewis ◽  
Patrick Benavidez ◽  
Mo Jamshidi
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Navigation ◽  
Unmanned Ground Vehicle ◽  
Virtual Testing ◽  
Ground Vehicle

Anti Intelligent Mine Unmanned Ground Vehicle Based on Reinforcement Learning

2021 ◽  
pp. 61-68
Author(s):  
Xiaoyao Tong ◽  
Yuxi Ma ◽  
Yuan Xue ◽  
Quanxin Zhang ◽  
Yuanzhang Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle

Unmanned ground vehicle simulation with the Virtual Autonomous Navigation Environment

2017 ◽  
Author(s):  
Christopher Goodin ◽  
Justin T. Carrillo ◽  
David P. McInnis ◽  
Christopher L. Cummins ◽  
Phillip J. Durst ◽  
...  
Keyword(s):  
Autonomous Navigation ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Vehicle Simulation

scholarly journals Autonomous navigation and mapping in a simulated environment

2021 ◽  
Author(s):  
Benjamin Christie ◽  
Osama Ennasr ◽  
Garry Glaspell
Keyword(s):  
Operating System ◽  
Autonomous Navigation ◽  
Three Dimensional ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Robot Operating System ◽  
Simulated Environment ◽  
Localization And Mapping ◽  
Environment Exploration ◽  
Rgbd Cameras

Unknown Environment Exploration (UEE) with an Unmanned Ground Vehicle (UGV) is extremely challenging. This report investigates a frontier exploration approach, in simulation, that leverages Simultaneous Localization And Mapping (SLAM) to efficiently explore unknown areas by finding navigable routes. The solution utilizes a diverse sensor payload that includes wheel encoders, three-dimensional (3-D) LIDAR, and Red, Green, Blue and Depth (RGBD) cameras. The main goal of this effort is to leverage frontier-based exploration with a UGV to produce a 3-D map (up to 10 cm resolution). The solution provided leverages the Robot Operating System (ROS).

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