Course

Unit 1

Introduction to autonomous robotics, terrestrial and aerial locomotion, mobile robot kinematic models, manoeuvrability, workspace, and kinematic control. Perception – non-visual sensors and algorithms, computer vision, image processing, feature extraction – interest point detectors, range data.

Lab experiments

1. Design and simulation of a biped robot. 2. MATLAB/Python programming for kinematic control of differential drive vehicle. 3. Line fitting and range data feature extraction.

Unit 2

Mobile robot localization, Noise and aliasing, belief representation, probabilistic map-based localization – Markoc and Kalman filter localization, Autonomous map building, SLAM paradigms – Extended Kalman filter, graph-based and particle filter. Sensorial, geometric and topological maps, robot collectives – Sensing, communication, formation control, localization and mapping.

Lab experiments

1. Line-based Kalman filtering for mobile robot localization, 2. Simultaneous localization and mapping based on Extended Kalman Filtering.

Unit 3

Planning and Navigation: Path planning. Graph search – Voronoi diagram, deterministic graph search, Dijkstra’s algorithm, A*, D* algorithm, Randomized graph search, Potential field path planning. Obstacle avoidance – Bug algorithm, Techniques viz. bubble band, curvature velocity, dynamic window approach, Schlegel approach, gradient method, etc., Mobile robots in practice, delivery robots, intelligent vehicles, mining automation, space robotics, underwater inspection, etc.

Lab experiments

1. Simulate a system of collective robots for arbitrary inputs and constraints, 2 Mobile robot path planning with global and local dynamic window approaches. 3. Noise rejection navigation simulation for mobile robot.

Course Objectives

• Familiarize with essential elements of robotic locomation.
• Comprehend challenges in realizing robotic locomation
• Familiarize with the concepts of path planning and navigation
• Impart knowledge on the basics of robot learning and collective robotics

Course Outcomes

Skills Acquired

Design of wheeled robots, implementation of localization, mapping, and path planning algorithms.

1. Roland Siegwart, Illah Nourbakhsh, and Davide Scaramuzza. (2011). Introduction to Autonomous Mobile Robots. 2^nd edition, The MIT Press.
2. Gregory Dudek, and Michael (2010). Computational Principles of Mobile Robotics. Second edition, Cambridge University press
3. Ulrich Nehmzow, (2012). Mobile Robotics: A Practical Introduction Second Edition.
4. Peter Corke (2017). Robotics, Vision and Control Fundamental Algorithms in MATLAB®. Second Springer
5. Howie Choset, Kevin Lynch, Seth Hutchinson, George Kantor, Wolfram Burgard, Lydia Kavraki, and Sebastian Thrun (2005) Principles of Robot Motion Theory, Algorithms, and Implementation, MIT press.
6. Sebastian Thrun, Wolfram Burgard, Dieter (2002) Probabilistic Robotics. The MIT press.
7. Steven LaValle. (2006). Planning Algorithms, Cambridge University Press.