UNI STU 3 SEM R: Gentle Introduction to Robot Motion Planning

Instructor

Professor Solmaz S. Kia
Department of Mechanical and Aerospace Engineering
Email: solmaz-at-uci.edu
Website: http://solmaz.eng.uci.edu

Description

A robot's ability to plan its movement without explicit human guidance is a basic prerequisite for robotic autonomy. The objective of motion planning algorithms is to enable an autonomous mobile robot to determine its movements in a cluttered environment to achieve various goals while avoiding collisions. This seminar series cover deterministic classical motion planning algorithms, including sensor-based planning, decomposition and search-based planning. The course intends to expose undergraduate students (Engineering and Computer Science) to solution approaches to problems that they may encounter in emerging technologies and disciplines such as autonomous driving and transportation, smart manufacturing, and general mechanical and aerospace robotic applications. 

The heuristic goals and outcomes of this class are

• introduce basic robotic motion planning problems and some of the basic classical solution approaches
• teach the students basic concepts from geometry, graph theory, algorithms, and Python programming and how to apply them to robot motion planning
• teach the students to use a numerical computing and programming environment to solve engineering problems
• introduce sufficient terminology and concepts so that interested students can get a start on independent reading of introductory robot motion planning and algorithms notes and literature

Text:

• The main reference for motion planning, which we will follow closely, is
  • Bullo and S. L. Smith. Lecture notes on robotic planning and kinematics. Copyrighted Material. (These are lecture notes available online. Check it out at http://motion.me.ucsb.edu/book-lrpk/)

The seminars will cover the following tentative topics:

• Introduction to Motion Planning
• Algorithms, Pseudocodes, and Complexity Notion in Algorithms
• A Gentle Introduction to Python Programming and Programming in Google Colabratory
• Sensor-based Motion Planning: Bug Algorithms
• Introduction to Graph Theory
• Gentle introduction to Bellman Optimality Principle and its Use in Motion Planning
• Graph Search and Pathfinding Algorithms and its Application in Robot Motion Planning
• Motion Planning via Decomposition and Search

• Motion Planning via Visibility Graph
• Sampling Based Motion Planning (PRM, RRT, RRT*)
• Tentative: Guest Lecture (depending on availability of speakers)

Grading: Pass/Not pass (Anyone enrolled in grade option will get automatic C-)

Expectation for pass: Attendance and class participation, completing programming assignments to the best of your ability

Academic Integrity Policy

No form of academic dishonesty will be tolerated. Academic misconduct, in its most basic form, is gaining or attempting to gain a grade, degree, or other academic accomplishment by any means other than through your own work. The formal policy states, "No student shall engage in any activity that involves attempting to receive a grade by means other than honest effort, and shall not aid another student who is attempting to do so." All academic integrity cases will be processed through the Office of Student Conduct under the Academic Honesty Policy. Please see Academic Integrity Policy (https://aisc.uci.edu/students/academic-integrity/) more information.