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Model Predictive Control for Unmanned Ground Vehicles Using Robot Operating System.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Model Predictive Control for Unmanned Ground Vehicles Using Robot Operating System./
作者:	Dekkata, Sai Charan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	133 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
標題:	Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28411325
ISBN:	9798516942679

Model Predictive Control for Unmanned Ground Vehicles Using Robot Operating System.
Dekkata, Sai Charan.

Model Predictive Control for Unmanned Ground Vehicles Using Robot Operating System. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 133 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: B.

Thesis (Ph.D.)--North Carolina Agricultural and Technical State University, 2021.

This item must not be sold to any third party vendors.

The purpose of the Husky A200 ground robot is to autonomously navigate the places where it is very hazardous for human beings to reach and operate, such as nuclear power plants and chemical industries. The aim is to navigate the ground robot autonomously with various sensors as the depth camera, 2D scanning laser, 3D Lidar, GPS, and IMU. The MPC improves the robot's motion, using a path planner for the robot's trajectory generation. The controller uses the current position from the Husky A200 given the way-points of the destination. It extracts the best possible route based on the recent events provided using IMU data and GPS. The use of global reference frame way-points is planned to create the appropriate path and the actions required to follow the motion planner's direction. The path planner depends on the active sensor data such as obstacles, and a feasible path is generated based on the sensor data. The desired trajectory consists of a set of way-points fit in a 3rd degree polynomial. They determine the path's feasibility for the Husky dynamics and a series of points generated with a certain velocity and acceleration profile. The MPC adjusts the Husky's lateral, longitudinal, yaw motions and attempts to approximate a continuous trajectory with discrete paths to command the behaviors. I use the kinematic model for Husky's kinematic motion and use the dynamic model for transient and steady-state characteristics. The camera captures the images and other data types through the computational framework used to build machine learning models. TensorFlow is used for deep learning and to identify and classify various objects around the Husky.

ISBN: 9798516942679Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Autonomous Vehicles

Model Predictive Control for Unmanned Ground Vehicles Using Robot Operating System.
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520 $a The purpose of the Husky A200 ground robot is to autonomously navigate the places where it is very hazardous for human beings to reach and operate, such as nuclear power plants and chemical industries. The aim is to navigate the ground robot autonomously with various sensors as the depth camera, 2D scanning laser, 3D Lidar, GPS, and IMU. The MPC improves the robot's motion, using a path planner for the robot's trajectory generation. The controller uses the current position from the Husky A200 given the way-points of the destination. It extracts the best possible route based on the recent events provided using IMU data and GPS. The use of global reference frame way-points is planned to create the appropriate path and the actions required to follow the motion planner's direction. The path planner depends on the active sensor data such as obstacles, and a feasible path is generated based on the sensor data. The desired trajectory consists of a set of way-points fit in a 3rd degree polynomial. They determine the path's feasibility for the Husky dynamics and a series of points generated with a certain velocity and acceleration profile. The MPC adjusts the Husky's lateral, longitudinal, yaw motions and attempts to approximate a continuous trajectory with discrete paths to command the behaviors. I use the kinematic model for Husky's kinematic motion and use the dynamic model for transient and steady-state characteristics. The camera captures the images and other data types through the computational framework used to build machine learning models. TensorFlow is used for deep learning and to identify and classify various objects around the Husky.
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