Intelligent Machines

Using a microprocessor and the Arduino ecosystem, students learn to design, build and use simple machines with motors and various sensors. There are three coordinated parts.

  • Hardware. The first 15 lectures are primarily about hardware: microprocessor, sensors and controls, and motors. Theory and skills are aimed at empowering students to design and build mechatronic devices on their own.
  • Planning, mapping, and localization: The next 11 lectures. These primarily involve robotic algorithms that can make use of the hardware.
  • Two-week project and competition take the place of 4 of the semester's nominal 30 lectures.  Teams build an autonomous robot and make it perform as well as they can.

Lectures will demonstrate the key theory and skills students need to be competent on their own. In tutorials, labs, homework and the project, students will practice and develop these skills. The exams will aim to test the students' understanding of the basic mechatronic systems discussed in these course components.

Learning Objectives

Through theory and labs, students in Intelligent Machines will gain introductory knowledge and functional skills related to:

  • Basic circuits (review), including voltage, current, power, and energy, impedance resistor circuits
  • Behaviour of circuit components, such as:
    • resistors,
    • capacitors,
    • diodes
    • transistors and relays,
    • switches, buttons and potentiometers,
    • Operational amplifiers, ll
    • Batteries and power supplies
    • Understand basic capabilities of microprocessors and their peripherals, such as:
    • Light-emitting diodes (LEDs);
    • Anolgue to digital  conversion
    •  and D to A via PWM
    •  RC-style PWM
  • Motor controllers and motors (Pulse Width Modulation (PWM), angular velocity, torque, power);
  • Rotory encoders and decoders (quadrature for angle measurement);
  • Communication protocols (I2C);
  • Interrupts and interrupt handlers;
  • Clock, timing; and timing signals; Ultrasonic and Infrared (IR) sensor;
  • Strain gauges and load cells -- Mechanics concepts needed for mechatronics;
  • force, moment, equilibrium, work, power, transmissions, friction, odometry, etc.
  • Filtering
  • Elements of control theory and practice;
  • feedback
  • open loop and closed loop control,

  • stability and instability

In the second part of the course:

  • Introduction to Fundamental Problems of Robotics (Point Robot)
  • Sensing:
    • Visual Sensors (Pinhole Camera, Camera model, RGB Images, 3D to 2D projection, Thermal Cameras, etc.)
  •  Planning
    • Search based Planning
    • Sampling based Planning
  • Mapping
    • Types of maps (Metric, Topological, Topo-metric)
    • Occupancy grid
    • Bayes filters for mapping
  • Localization
    • Beacon based / GPS for localization
    • Bayes filters for Localization

Learning Outcomes

Skills students will gain and demonstrate the knowledge above.

  • Ability to intelligently discuss the topics above in the context of robotics generally
  • Ability to design, build, and test mechatronic devices involving the concepts and components above to make an elementary autonomous mobile robot.
  • Ability to perform simple calculations related to deployment of the various hardware components and check for  for successful, or not, implementation.
  • General development of problem solving and debugging skills. Trial and error, hypothesis and testing, common sense, elimination of alternatives, the need for persistence, etc.

Students will develop understanding of what resources are available, including LLMs, data sheets, YouTube videos, etc.

Specific recommended  references.

  • Gregory Dudek and Michael Jenkin, Computational Principles of Mobile Robotics, Edition 2, Cambridge University Press (Chapters 2, 3, 4, 5, 6, 9)
  • Katsuhiko Ogata, Modern Control Engineering, Pearson Publishers - Francesco Bullo and Stephen L. Smith, Robotic Planning and Kinematics, available online at: http://lrpk.ricopic.one/#ch:preface
  • Howie Choset, Kevin Lynch, Seth Hutchinson, George Kantor, Wolfram Burgard, Lydia Kavraki, and Sebastian Thrun, Principles of Robot Motion Theory, Algorithms, and Implementation, available online at:http://mathdep.ifmo.ru/wp-content/uploads/2018/10/Intelligent-Robotics-andAutonomous-Agents-series-Choset-H.-et-al.-Principles-of-Robot-Motion-Theory-Algorithms-andImplementations-MIT-2005.pdf

Additional References

  • Kenji Hata and Silvio Savarese, Camera models, course notes available online at: https://web.stanford.edu/class/cs231a/course_notes/01-camera-models.pdf
  • Robert J. Shilling, Fundamentals of Robotics: Analysis and Control, Pearson, 2015
  • Gilbert Strang, Linear Algebra and its Applications, Cengage Publications
  • John J. Craig, Introduction to Robotics: Mechanics and Control, Pearson, 2008
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