Autonomous Robots Lab
  • Home
  • News
  • Research
    • Autonomous Navigation and Exploration
    • Fixed-Wing UAVs
    • Agile and Physical Interaction Control
    • Localization and 3D Reconstruction
    • Subterranean Robotics
    • Collision-tolerant Aerial Robots
    • Marine Robotics
    • Intelligent Mobility >
      • Student Projects
      • Electric Bus Datasets
    • Robotics for Nuclear Sites
    • Degerminator
    • Autonomous Robots Arena
    • Code
    • Media
    • Research Presentations
    • Projects
  • Publications
  • Group
    • People
    • Research Collaborators
    • Positions
  • Education
    • Introduction to Aerial Robotics >
      • Online Textbook >
        • Modeling >
          • Frame Rotations and Representations
          • Multirotor Dynamics
        • State Estimation >
          • Inertial Sensors
          • Batch Discrete-Time Estimation
          • The Kalman Filter
        • Flight Control >
          • PID Control
          • LQR Control
          • Linear Model Predictive Control
        • Motion Planning >
          • Holonomic Vehicle BVS
          • Dubins Airplane
          • Collision-free Navigation
          • Structural Inspection Path Planning
        • Simulation Tools >
          • Simulations with SimPy
          • MATLAB & Simulink
          • RotorS Simulator >
            • RotorS Simulator Video Examples
      • Lecture Slides
      • Literature and Links
      • RotorS Simulator
      • Student Projects
      • Homework Assignments
      • Independent Study
      • Video Explanations
      • Syllabus
      • Grade Statistics
    • Autonomous Mobile Robot Design >
      • Lecture Slides
      • Semester Projects
      • Code Repository
      • Literature and Links
      • RotorS Simulator
      • Video Explanations
      • Resources for Semester Projects
      • Syllabus
    • Robotics for DDD Applications
    • CS302 - Data Structures
    • Student Projects >
      • Robot Competitions
      • Undergraduate Researchers Needed
      • ConstructionBots - Student Projects
    • EiT TTK4854 - Robotic Ocean Waste Removal
    • Aerial Robotic Autonomy >
      • Breadth Topics
      • Deep-dive Topics
      • Literature
    • Robotics Seminars
    • Robotics Days
    • Outreach >
      • Drones Demystified! >
        • Lecture Slides
        • Code Repository
        • Video Explanations
        • RotorS Simulator
        • Online Textbook
      • Autonomous Robots Camp >
        • RotorS Simulator
      • Outreach Student Projects
    • BadgerWorks >
      • General Study Links
      • Learn ROS
      • SubT-Edu
  • Resources
    • Autonomous Robots Arena
    • Robot Development Space
  • Contact

Autonomous Mobile Robot Design

Level: CS491/CS691 
Overview: The goal of this course will be to introduce students into the holistic design of autonomous robots - from the mechatronic design to sensors and intelligence. The course will have five "teaching blocks", namely: a) actuation and robot locomotion, b) sensing and robot perception, c) control and robot guidance, d) motion planning and autonomous navigation, e) remote control and robot GUI and will be 100% project-driven. The students will be organized in teams and each team will design a robot in order to solve a specific application and challenge. As an example: a team project might be "aerial robots for multi-modal characterization of industrial facilities" or "robots with advanced traversability abilities for mountain operations". The final challenges will be derived based on real-requirements of federal agencies or needs of specific industries. Overall the course aims to develop fundamental knowledge in robotics and engage both undergraduate and graduate students in cutting-edge research. 

Key-features of the course and what makes it different:
  • Absolutely project driven: your work will be to learn from the lectures and apply what you learn to design a robot. You will demonstrate your robot at the end of the semester.
  • Robot design driven by real-life needs: you will be asked to design a robot to solve a real challenge.
  • Robot design driven by specific budget: you will be offered a specific amount as a team (max $2K) to create your robot. You will have to come up with the best solution given the budget. 
  • Holistic experience: robotics is a multi-disciplinary science. This course asks you to be specific and pioneering in what you excel but also capable to deal with a holistic design process. Some of you will focus on perception, others on control but at the end you will have to work as a team.
​
The material of the course will be available at the beginning of Fall 2016.
​
Please send me an e-mail (kalexis@unr.edu) if you are interested to be participate in this course.
Picture
Picture
Picture

Course Material

Lecture Slides
Code Repository 
Picture
Picture
Semester Projects
Simulator Tools
Picture
Picture
Literature and Links
Video Explanations
Picture


Testing in the Autonomous Robots Arena
Picture


Picture
Course Syllabus
Picture

Course Projects Synopsis

Project #1: GPS-denied Autonomous Car Localization in Visually-degraded Conditions
Description: GPS-denied localization employing vision and LiDAR sensors is an extensively studied field currently working reliably in simple, featurefull or geometrically structured environments. However, when in visually-degraded conditions and ill-conditioned geometry the problems remains particularly challenging. The goal of this project is to enable GPS-denied Simultaneous Localization And Mapping (SLAM) for autonomous cars navigating subject to rain, snow or ice. 

Research Tasks:
  • Task 1: Vision, NIR, LiDAR System Integration
  • Task 2: Vision, NIR, LiDAR Sensor Fusion
  • Task 3: Dataset collection and groundtruth stamping using GPS
  • Task 4: Field experiments and evaluation​​ ​

Project #2: Change Detection for Autonomous Driving
Description: When a vehicle navigates continuously within a certain area, exploiting its previous map to localize robustly within it and pre-plan its actions leads to optimized performance. For this process to be reliable, spatio-temporal change detection has to take place. However, change detection is challenging both in terms of correlating the input data and maps, as well as in terms of map scalability.

Research Tasks:
  • Task 1: Change detection in images
  • Task 2: Volumetric mapping
  • Task 3: Change detection in volumetric maps
  • Task 4: Semantic change classification using convolutional neural nets
  • Task 4: Dataset collection and groundtruthing
  • Task 5: Field experiments and evaluation​ ​

Project #3: Robotic Inspection of Mines
Description: Mine inspection corresponds to a major challenge due to the difficult environments and the often visually-degraded conditions. This project refers to the development of an aerial and ground robotic system that aims to enable systematic 3D mapping and semantic classification.

Research Tasks:
  • Task 1: Platform development (ideally based on existing robots at the lab)
  • Task 2: Volumetric and surface mapping
  • Task 3: Aerial - to -ground robot collaboration
  • Task 4: Dataset collection and groundtruthing
  • Task 5: Field experiments and evaluation​ ​

​​

Study links

• Lecture Slides
• Literature and Links
​• Video Explanations






Open-Source Code

• Code Repository: https://github.com/unr-arl/autonomous_mobile_robot_design_course
• NBVPlanner: https://github.com/ethz-asl/nbvplanner


More

• Introduction to Aerial Robotics course
Proudly powered by Weebly