ECE 470
Introduction to Robotics

Section Type Times Days Location Instructor
AB1 LAB 0900 - 1050 T   3071 ECE Building  Jifei Xu
AB2 LAB 0900 - 1050 R   3071 ECE Building  Lingyu Ma
AB3 LAB 1500 - 1650 T   3071 ECE Building  Shenyu Liu
AB4 LAB 1500 - 1650 R   3071 ECE Building  Jifei Xu
AB5 LAB 1400 - 1550 W   3071 ECE Building  Shenyu Liu
AL1 LEC 1230 - 1350 T R   1015 ECE Building  Seth Hutchinson

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Official Description Fundamentals of robotics including rigid motions; homogeneous transformations; forward and inverse kinematics; velocity kinematics; motion planning; trajectory generation; sensing, vision; control. Course Information: Same as AE 482 and ME 445. 4 undergraduate hours. 4 graduate hours. Prerequisite: One of MATH 225, MATH 286, MATH 415, MATH 418.
Subject Area Robotics, Vision, and Artificial Intelligence
Course Prerequisites Credit in MATH 225 or MATH 286 or MATH 418 or MATH 415
Course Directors Seth Andrew Hutchinson
Detailed Description and Outline


  • Introduction: Historical development of robots; basic terminology and structure; robots in automated manufacturing
  • Rigid Motions and Homogeneous Transformation: Rotations and their composition; Euler angles; roll-pitch-yaw; homogeneous transformations; Matlab and Mathematica code for symbolic and numerical computation
  • Forward Kinematics: Common robot configurations; Denavit-Hartenberg convention; A-matrices; T-matrices; examples
  • Inverse kinematics: Planar mechanisms; geometric approaches; spherical wrist
  • Velocity kinematics: Angular velocity and acceleration; The Jacobian; singular configurations; singular values; pseudoinverse; manipulability
  • Motion planning: Configuration space; artificial potential fields; randomized methods; collision detection
  • Trajectory generation: Joint space interpolation; polynomial splines; trapezoidal velocity profiles; minimum time trajectories
  • Feedback control: Actuators and sensors; transfer functions; tracking and disturbance rejection; PID control; feed forward control; resolved motion rate control
  • Vision-based control: The geometry of image formation; feature extraction; feature tracking; the image Jacobian; visual servo control Advanced Topics (one or more of the following depending on the instructor): Lagrangian dynamics; parallel robots; mobile robots; force sensing and force control; machine learning; advanced topics in vision; student projects; other

Same as GE/ME 470 and CS 443.

Lab Projects

Teach pendant programming; off-line programming; workcell generation; computer/robot interfacing; kinematics; symbolic math packages for robot kinematics; inverse kinematics; motion planning; trajectory planning; feedback control; camera calibration; feature detection and tracking; vision-based manipulation


Spong, Hutchinson, and Vidyasagar, Robot Dynamics and Control, New York: John Wiley, 2005.

Course Goals

This course serves as a technical elective for computer engineering and electrical engineering majors. The goal of this course is to introduce students to the basic concepts in robotics that (a) provide prerequisite knowledge for follow-on courses, and (b) provide essential knowledge of the field that would be required by a practicing engineer who must deal with automation. This course includes a significant laboratory component.

Instructional Objectives

By the time of the first examination:

  1. The historical development of robots (h)
  2. Basic terminology and structure (j)
  3. Robots in automated manufacturing (h)
  4. Rotation matrices and their composition (a, m)
  5. Euler angles (a, m)
  6. Roll-pitch-yaw angles (a, m)
  7. Angular velocity and acceleration (a, m, n)
  8. Homogeneous transformations (a, m, n)
  9. Common robot configurations (a)
  10. The Denavit-Hartenberg convention (a, m)
  11. A-matrices (a, m)
  12. T-matrices (a, m)
  13. Forward kinematics of open kinematic chains (a, m)
  14. Inverse Kinematics of planar mechanisms (a, m)
  15. Geometric approaches to inverse kinematics (a, m)
  16. Inverse kinematics of the spherical wrist (a, m)
  17. The manipulator Jacobian matrix (a, m, n)

By the time of the 2nd exam:

  1. Singular configurations (a, m, n)
  2. Manipulability (a, m, n)
  3. Singular values (a, m, n)
  4. Pseudoinverse of the Jacobian and its use (a, m, n)
  5. Computer vision in automation (h, j)
  6. Perspective geometry, pin-hole lens approximation (a, m)
  7. Stereo vision by triangulation (a, m)
  8. 2D discrete convolution (a, m, n)
  9. 2D digital linear filters (a)
  10. Edge detection (a, m)
  11. Histograms (a, l, m)
  12. Threshold selection (a, l, m)
  13. Moments (a)

Last updated: 9/9/2014