Lecture Progress

Week Topics Slides Notes
Week 1
(25 - 29 Aug)		 Course Introduction; Introduction to Robotic Systems. Lecture1
Lecture2		 Python Revision - Unit 1-4
Week 2
(1 – 5 Sep)		 Analysis of Algorithms I: Algorithm Performance, The big-Oh Notation, Worst-case and Average-case performance.
Analysis of Algorithms II: Complexity Calculations examples.		 lecture3
lecture4		 Chapter 9
Week 3
(8 Sep – 12 Sep)		 Analysis of Algorithms III: Exercises.
Analysis of Algorithm IV: Time and Space tradeoff.		    
Week 4
(15 – 19 Sep)		 Complexity and Intractability: P, NP, NP-Complete.    
Week 5
(22 – 26 Sep)		 Sorted and Unsorted Lists.
Stacks and Queues.

(Quiz 1 – Tuesday 23 Sep).		    
Week 6
(29 Sep – 3 Oct)		 State / Configuration Space; Introduction to Uninformed Graph Search: Depth-first search and Breadth-first search.
Dijkstra’s algorithm.		    
Week 7
(6 – 10 Oct)		 Priority Queue (Heap).
Binary Search Recap; Sorting Algorithms: Selection, Bubble; Quick Sort.		    
Week 8
(13 – 17 Oct)

*17 Oct - Mid Grade Due		 Merge Sort and Heap Sort.
Uniform Cost Search; Informed Graph Search: A* (optional: D*).		    
Week 9
(20 – 24 Oct)		 Introduction to Continuous C-space; Exact geometric planning methods: Visibility graphs, Cell Decomposition.
Sampling-Based Planning; Probabilistic Roadmaps (PRM); Rapidly-Exploring Random Trees (RRT).


(Midterm Exam).		    
Week 10
(27 – 31 Oct)

*31 Oct Last day to drop w/”W”		 Potential Field Methods.



(Quiz 2 – Tuesday 28 Oct).		    
Week 11
(3 – 7 Nov)		 Car-Like Robots (Nonholonomic Planning); From A* to Kinematic A*; Motion primitives: Dubins & Reeds–Shepp curves.
Lattice-based planning; Path Smoothing & Splines.		    
Week 12
(10 – 14 Nov)
*Abu Dhabi Autonomous Week 10-15 Nov		 Introduction to AVLite, a software stack of autonomous vehicles; Global vs Local Planning.

A2RL race field visit at Yas Marina Circuit.
(Assignment Issue – Thursday, 11 Nov).		    
Week 13
(17 – 21 Nov)		 Drivable Trajectories for Cars: Frenet Frame Basics.
(Quiz 3 – Thursday 20 Nov).		    
Week 14
(21 – 25 Nov)		 Behavior Planning Methods: Finite State Machines (FSMs);

Behavior Trees (BTs)
(Assignment Deadline: Friday 25 Dec at 11:59 PM).		    
Week 15
(1 – 5 Dec)

5 Dec - Last Day of Classes
18 Dec - Final Grades Publish		 Putting it all together: Global planning (A*, PRM, RRT); Behavior planning (FSM/BT); Local planning (splines, Frenet).    

Lab Progress

Week Topic Progress
Week 1 No Lab  
Week 2 Safety instructions and Lab Rules. Assembling TurtleBot3 Lab 0
Week 3 Intro to ROS 2, RViz & Gazebo using turtlebot3 Lab 1
Week 4 Building and Moving the turtlebot - LAB A  
Week 5 Line & Waypoint Following - Lidar reading  
Week 6 Simultaneous Localisation and Mapping (SLAM) using TurtleBot3 Lidar  
Week 7 Path planning in 2D occupancy grid using Djikstra algorithm  
Week 8 No Lab  
Week 9 Path planning in 2D occupancy grid using A* algorithm  
Week 10 Path planning in 2D occupancy grid using RRT ( Rapidly exploring Random Tree) algorithm  
Week 11 Path planning in 2D occupancy grid using Potential Fields  
Week 12 project declaration  
Week 13 Project implementation  
Week 14 Final Project Demonstration  
Week 15 No Lab