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    A novel low-cost autonomous 3D LIDAR system

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    Author
    Dial, Ryker L.
    Chair
    Bogosyan, Seta
    Hatfield, Michael
    Committee
    Lawlor, Orion
    Keyword
    Space mining
    Robots
    Automation
    Autonomous robots
    Design and construction
    Mines and mineral resources
    Optical radar
    Mining engineering
    Mining machinery
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/11122/8716
    Abstract
    To aid in humanity's efforts to colonize alien worlds, NASA's Robotic Mining Competition pits universities against one another to design autonomous mining robots that can extract the materials necessary for producing oxygen, water, fuel, and infrastructure. To mine autonomously on the uneven terrain, the robot must be able to produce a 3D map of its surroundings and navigate around obstacles. However, sensors that can be used for 3D mapping are typically expensive, have high computational requirements, and/or are designed primarily for indoor use. This thesis describes the creation of a novel low-cost 3D mapping system utilizing a pair of rotating LIDAR sensors, attached to a mobile testing platform. Also, the use of this system for 3D obstacle detection and navigation is shown. Finally, the use of deep learning to improve the scanning efficiency of the sensors is investigated.
    Description
    Thesis (M.S.) University of Alaska Fairbanks, 2018
    Table of Contents
    Chapter 1. Introduction -- 1.1. Purpose -- 1.2. 3D sensors -- 1.2.1. Cameras -- 1.2.2. RGB-D Cameras -- 1.2.3. LIDAR -- 1.3. Overview of Work and Contributions -- 1.4. Multi-LIDAR and Rotating LIDAR Systems -- 1.5. Thesis Organization. Chapter 2. Hardware -- 2.1. Overview -- 2.2. Components -- 2.2.1. Revo Laser Distance Sensor -- 2.2.2. Dynamixel AX-12A Smart Serial Servo -- 2.2.3. Bosch BNO055 Inertial Measurement Unit -- 2.2.4. STM32F767ZI Microcontroller and LIDAR Interface Boards -- 2.2.5. Create 2 Programmable Mobile Robotic Platform -- 2.2.6. Acer C720 Chromebook and Genius Webcam -- 2.3. System Assembly -- 2.3.1. 3D LIDAR Module -- 2.3.2. Full Assembly. Chapter 3. Software -- 3.1. Robot Operating System -- 3.2. Frames of Reference -- 3.3. System Overview -- 3.4. Microcontroller Firmware -- 3.5. PC-Side Point Cloud Fusion -- 3.6. Localization System -- 3.6.1. Fusion of Wheel Odometry and IMU Data -- 3.6.2. ArUco Marker Localization -- 3.6.3. ROS Navigation Stack: Overview & Configuration -- 3.6.3.1. Costmaps -- 3.6.3.2. Path Planners. Chapter 4. System Performance -- 4.1. VS-LIDAR Characteristics -- 4.2. Odometry Tests -- 4.3. Stochastic Scan Dithering -- 4.4. Obstacle Detection Test -- 4.5. Navigation Tests -- 4.6. Detection of Black Obstacles -- 4.7. Performance in Sunlit Environments -- 4.8. Distance Measurement Comparison. Chapter 5. Case Study: Adaptive Scan Dithering -- 5.1. Introduction -- 5.2. Adaptive Scan Dithering Process Overview -- 5.3. Coverage Metrics -- 5.4. Reward Function -- 5.5. Network Configuration -- 5.6. Performance and Remarks. Chapter 6. Conclusions and Future Work -- 6.1. Conclusions -- 6.2. Future Work -- 6.3. Lessons Learned -- References.
    Date
    2018-05
    Type
    Thesis
    Collections
    Engineering

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