<p>School of Electrical, Computer and Energy Engineering</p><p>M.S. Final Oral Defense</p><p>Modeling and Control for Vision Based Rear Wheel Drive Robot and Solving Indoor SLAM Problem Using LIDAR</p><p> by Xianglong Lu 7/19/2016 2:30PM Room GWC 487</p><p>Committee: Dr. Armando A. Rodriguez (chair) Dr. Spring Berman Dr. Panagiotis Artemiadis</p><p>Abstract</p><p>To achieve the ambitious long-term goal of a fleet of cooperating Flexible Autonomous </p><p>Machines operating in an uncertain Environment FAME, this thesis addresses several critical modeling, design, control objectives for rear-wheel drive ground vehicles. </p><p>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.</p><p>A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities.</p><p>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</p><p>(1) xv 11 hacked LIDAR to implement SLAM algorithm (hector mapping)</p><p>(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. </p><p>(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. </p><p>(4) an Arduino motor shield for inner-loop motor speed control. </p><p>(5) a Raspberry Pi 2 computer board for more demanding vision based cruise-position- directional-outer-loop control and LIDAR data processing. </p><p>(6) a Raspberry Pi camera for outer-loop cruise-position-directional control. </p><p>(7) a Futaba S3003 standard servo for front wheel steering </p><p>(8) a Mallofusa 2 DOF Pan Tilt for a flexible adjustment of camera position</p><p>(9) a FT232RL Universal Asynchronous Receiver/Transmitter (UART) to translate data from serial port (LIDAR data output) to USB port </p><p>(10) a voltage regulator: the power supply circuit was redesigned to provide 5V power supply (regulated from 7.2V). </p><p>(11) a potentiometer to adjust RPM of motor in LIDAR unit (LIDAR provides valid data only when its RPM is around 280)</p><p>(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.</p><p>Kinematic and dynamical models are examined. Suitable models are used to develop inner- and outer-loop control laws.</p><p>All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following summarizes the key hardware demonstrations presented and analyzed:</p><p>(1) Cruise (v,) control along a line,</p><p>(2) Cruise (v,) control along a curve, </p><p>(4) Planar (x, y) Cartesian stabilization</p><p>(3) Finish the track with camera pan tilt structure in minimum time,</p><p>(4) finish the track without camera pan tilt structure in minimum time,</p><p>(5) Vision based tracking performance with different cruise speed,</p><p>(6) Vision based tracking performance with different camera fixed look-ahead distance,</p><p>(7) Vision based tracking performance with different delay from vision subsystem,</p><p>(8) Manually remote controlled robot to perform indoor SLAM,</p><p>(9) Autonomously line guided robot to perform indoor SLAM.</p><p>For most cases, hardware data is compared with, and corroborated by, model-based simulation data.</p><p>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.</p>