Autonomous Control of Robotic Systems
CMPE Degree: This course is Not Applicable for the CMPE degree.
EE Degree: This course is Not Applicable for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Group(s) / Course Type(s): Systems and Controls
Catalog DescriptionFundamental issues associated with autonomous robot control. Emphasizes biological perspective that forms the basis of many current developments in robotics.
Student OutcomesIn the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.
1. ( Not Applicable ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2. ( Not Applicable ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3. ( Not Applicable ) An ability to communicate effectively with a range of audiences
4. ( Not Applicable ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5. ( Not Applicable ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6. ( Not Applicable ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7. ( Not Applicable ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Strategic Performance Indicators (SPIs)
1. Introduction: Anatomy of a Robot: Classification of Robots; Robot Configurations; Robot Components; Performance Characteristics.
2. Foundations: 2D and 3D affine transformations. Jacobian matrices. Simulation tools.
3. Kinematics: Modeling kinematic chains, Forward kinematics, Inverse kinematics
4. Perception: Simple pinhole camera model, Basics in camera calibration, Triangular active sensing, Color space, Image filtering algorithms to reduce noise, Edge detection, Hough transform for lines and circles
5. Reactive Behaviors: Feedback control. Basic navigation algorithms based on recognized landmarks. Obstacle avoidance. Path following and boundary following. Simple reactive behaviors to object detected by computer vision.
6. Motion and Path Planning: Distance transform, Breadth first search, the A* algorithm, Potential field-based method.
7. Dynamics and Control: Rigid body dynamics equations, Controller design for quadrotors and underwater vehicles.