ANALYSIS of Aluminum NITIRDE (Aln) and GRADED ALUMINUM GALLIUM NITRIDE (Algan) THIN FILM s2
School of Electrical, Computer and Energy Engineering
M.S. Final Oral Defense
Modeling and Control for Vision Based Rear Wheel Drive Robot and Solving Indoor SLAM Problem Using LIDAR
by
Xianglong Lu
7/19/2016
2:30PM
Room GWC 487
Committee:
Dr. Armando A. Rodriguez (chair)
Dr. Spring Berman
Dr. Panagiotis Artemiadis
Abstract
To achieve the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment FAME, this thesis addresses several critical modeling, design, control objectives for rear-wheel drive ground vehicles. Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform that can be used for conducting FAME research.
A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities.
The augmented vehicle (FreeSLAM) costs less than $500 but offers the capability of commercially available vehicles costing over $2000. More specifically, the rear-wheel drive vehicle was augmented with the following
(1) xv 11 hacked LIDAR to implement SLAM algorithm (hector mapping)
(2) magnetic wheel encoders and an inertial measurement unit (IMU) to facilitate rear wheel drive vehicle inner-loop speed control as well as outer loop position and directional control.
(3) an Arduino Uno open source microcontroller development board for encoder – IMU based speed inner-loop control and encoder-camera based cruise-position-directional outer-loop control.
(4) an Arduino motor shield for inner-loop motor speed control.
(5) a Raspberry Pi 2 computer board for more demanding vision based cruise-position-directional-outer-loop control and LIDAR data processing.
(6) a Raspberry Pi camera for outer-loop cruise-position-directional control.
(7) a Futaba S3003 standard servo for front wheel steering
(8) a Mallofusa 2 DOF Pan Tilt for a flexible adjustment of camera position
(9) a FT232RL Universal Asynchronous Receiver/Transmitter (UART) to translate data from serial port (LIDAR data output) to USB port
(10) a voltage regulator: the power supply circuit was redesigned to provide 5V power supply (regulated from 7.2V).
(11) a potentiometer to adjust RPM of motor in LIDAR unit (LIDAR provides valid data only when its RPM is around 280)
(12) a TP-LINK Wi-Fi adapter to support remote control of robot and wireless data transmission
both the Arduino and Raspberry platforms are low cost, well supported (software wise) and easy to use.
Kinematic and dynamical models are examined. Suitable models are used to develop inner- and outer-loop control laws.
All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following summarizes the key hardware demonstrations presented and analyzed:
(1) Cruise (v,q) control along a line,
(2) Cruise (v,q) control along a curve,
(4) Planar (x, y) Cartesian stabilization
(3) Finish the track with camera pan tilt structure in minimum time,
(4) finish the track without camera pan tilt structure in minimum time,
(5) Vision based tracking performance with different cruise speed,
(6) Vision based tracking performance with different camera fixed look-ahead distance,
(7) Vision based tracking performance with different delay from vision subsystem,
(8) Manually remote controlled robot to perform indoor SLAM,
(9) Autonomously line guided robot to perform indoor SLAM.
For most cases, hardware data is compared with, and corroborated by, model-based simulation data.
In short, the thesis uses low-cost self-designed rear-wheel drive robot to demonstrate many capabilities that are critical in order to reach the longer-term FAME goal.