Control, Autonomy, Planning and Navigation

This course provides an in-depth study of the principles and techniques underlying the control and autonomous navigation of robotic systems. Students will explore the kinematics and dynamics of robots, feedback control methods, path planning, obstacle avoidance, localization, and mapping. The course also examines the integration of these concepts to enable autonomous systems to perceive, navigate, and make decisions in complex and dynamic environments.

Course Overview

Info not available

Learning Objectives

After successful completion of this class, the students will be able to:

  1. Students will understand and explain the fundamental principles of autonomy, including the role of uncertainty in robot perception and action. This includes a grasp of how probabilistic approaches differ from deterministic ones and why they are critical in robotics. | Understand
  2. Students will analyze various aspects of robotic systems, such as environmental unpredictability, sensor limitations, robot actuation, model inaccuracies, and computational constraints. They will learn to model these aspects probabilistically to handle real-world uncertainty | Apply
  3. Students will apply algorithms to solve specific problems in robotics, such as mobile robot localization. This involves using algorithms like Bayes filters for posterior estimation over robot locations, demonstrating a practical application of theoretical concepts. | Create
  4. Students will critically evaluate the effectiveness of probabilistic algorithms in addressing challenges in robotics, such as localization failure and map building without global positioning systems. | Evaluate

Learning Outcomes

Info not available

  • Gregory Dudek and Michael Jenkin, Computational Principles of Mobile Robotics, Edition 2, Cambridge University Press
  • Probabilistic Robotics, by Thrun, Fox, and Burgard

Additional Readings

Info not available

Assessment and Grading

The assessments will comprise of the following:

  • Homework (10%)
  • Quiz (20%)
  • Course Project (20%)
  • Lab (10%)
  • Mid Sem (15%)
  • End Sem (20%)
  • Participation & Attendance (5%)

# All homeworks and assignments need to be uploaded to LMS in the format given.

Please note the following:

  • 10% will be deducted per working day late for a submission.
  • Any re-grade requests for homeworks or assignments or exams must be made within one week of the return in class / lab.
Top