What you will learn
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Understand Basics of Ubuntu, ROS, and Python
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Build UR5 robot in rviz, control robot, and visualize the motion in Gazebo
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Understand the sensors required for robot localization, mapping, and navigation
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Build Robot Localization and Mapping algorithms
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Gain experience on different path planning algorithms and build motion planning algorithms
About this Micro-Specialization
The Robotics Specialization introduces you to the principles of the robot arm, mobile robot movement, how robots understand their environment, and the way they alter their moves to keep away from obstacles, navigate tough terrains and achieve complicated duties including independent navigation. You may be uncovered to real-world examples of the way robots had been implemented in industries and the way they've made advances in human transportation, and what their destiny capabilities may be. The guides construct toward a capstone wherein you'll discover ways to software a robot to carry out many moves, including robot arm movement and mobile robot movement.
Applied Projects
Every Micro-specialization includes hands-on projects to ensure the practical & project-based learning experience. In this micro-specialization, learners will work on UR5 robot arm motion control, Particle Filter based Localization, Occupancy Grid Mapping, RRT path planning, and Motion Planning algorithms as their projects. You will build detailed mathematical models with fundamental equations, develop algorithms & control logic.
Start with Python Fundamentals & motivation to master Python
Learning overview: Week 1
Kickstart the Python learning, lay the solid foundation & everyone is a beginner.
We will start with understanding the wide range of applications Python is being used in Industries, Startup's & Governments etc., We shall build the clear motive & motivations to learn the language during this internship & the outcomes you can get.
You will begin to learn the Python programming with live coding sessions on topics such as operators, data types, Operations in Python (Integers and floats, Strings, Type Conversions & Booleans), Statements and Loops ( IF, IF - else & elif), Functions (Arguments, Return & In-built functions), Loops ( for & while), break & continue.
Practice with examples & gain confidence to start projects
Learning overview: Week 2
During Week 2, we will be exploring the vast and fabulous domains of Intermediate and Advanced Python programming with hands-on coding. Interns will go through solid theory as well as practical sessions for each new topic to gain confidence & expertise to build the real-world projects in ahead of week 3 & 4.
Topics such as Lists (Operations, Functions, Slices & Comprehensions), Dictionaries, Tuples, Sets, Arrays, Exceptions, Advanced File Handling, Modules & Packages, Libraries in Python (Numpy, Pandas & Matplotlib), Plots & Matrix Operations will be covered during the week 2.
Kickstart the ROS basics (Programming)
Learning overview: Week 3
Objective of week 3 is to lay the solid foundation to get you acquainted with ROS, it’s industrial applications & getting you to start using the framework. We will help you install the necessary software, set the work platform & software. We will further help you begin the programming in ROS on topics such as ROS file system, ROS packages, topics, nodes, and program publisher, subscriber, services and client in ROS.
Foundation to Robotics
Learning overview: Week 4
During week 4, you will be introduced to robotics, types of robots, robotic arms, types of robotic arms. We will teach the required fundamentals such as rigid body motion, degrees of freedom, configuration space and robot links & joints, kinematics, forward kinematics, inverse kinematics, and rotation matrices which will be necessary for weeks 5 and 6 to build the real-world industry projects.
Major Project: Create a Universal Robot UR5 in ROS
Learning overview: Week 5
Practice during week 5 will be focused to understand UR5 robotic arms, links, and joints. We shall program the URDF file to represent the UR5 robot’s links and joints. We will use the tools such as rviz and gazebo to visualize the robot and the robot's motion. You will be further taught by the mentor on forward and inverse kinematics of the UR5 robot to derive and program using Python to create the robot's motion in gazebo.
Major Project: Create a Delta Robot in ROS
Learning overview: Week 5
During week 6, you will be given a problem statement to building a delta robot in rviz and gazebo to visualize the robot’s motion by programming the inverse kinematics in Python. Participants will have to understand the application and build the algorithm for the application & prepare a document of obtained results. This assignment will challenge your skills you have gained during the first 5 weeks. This is an important activity to learn the application of tools and also an opportunity to understand, how to build and analyze a system. (You will be supported by the mentor in case of any queries)
Mobile Robots, Sensors & 1D Bayes filter localisation algorithm
Learning overview: Week 7
Introduction to mobile robots, types of mobile robots (Wheeled, Tracked, Legged, AIr-based & Water based robot), types of wheeled robots and their locomotion (Single wheeled, two wheeled, three wheeled, Four wheeled & Five or more wheeled robots), Types of wheels (Standard/Fixed, Orientable Ball and Omni wheel).
Introduction to a sensors and its types (Active and Passive), Introduction to Odometry sensor, It’s need, working and error propagation. Introduction to a Laser sensor, it’s need, working, Laser parameters (Maximum, Minimum, Accuracy, Resolution, Scan Area) and Laser gaussian error (1D and 2D). Introduction to Localization and need for localization. Basics of probability (Joint Distribution & Conditional Probability).
Introduction Baye's filter, hand calculation to estimate if a door Is open or closed using Baye’s filter . Baye's filter 1D localization algorithm. Programming 1D robot localization based on robot sensing and movement and Analysis of robot localization accuracy based on the grid size.
2D Bayes filter localisation algorithm
Learning overview: Week 8
Introduction to 2D Baye's filter localization, 2D Baye's filter algorithm, Grid based representation of the environment and Robot orientation pose representation (x, y, theta). Programming 2D Baye's filter localization algorithm to estimate robots position based on robot’s movement and Observation and Visualization of robot position tracking based on given tag position. Introduction to ROS bag file and creating ROS bag file for the given ROS topics.
Monte Carlo localisation algorithms
Learning overview: Week 9
Introduction to particle filter or Monte Carlo localization, Robot localization based on particle sampling, resampling, motion update, estimate robot’s new belief, given the previous belief and Particle filter localization algorithm. Programming robot with Monte Carlo localization, odometry noise, laser noise, number of particles and particle weights. Analysis of robot position estimation based on number of particles and robot motion to understand the time taken and error value of the algorithm.
Robot mapping & occupancy grid mapping algorithm
Learning overview: Week 10
Introduction to robot mapping and application of robot mapping in real world scenarios. Introduction to occupancy grid mapping, Occupancy grid mapping algorithm, Laser scan method of identifying free and occupied spaces, Bresenham's line algorithm to convert laser scan data to obstacle representation.
Introduction to Quaternion coordinate system and conversion of Quaternion coordinate system to Euler angles. Programming occupancy grid mapping algorithm to estimate to scan the environment and calculate the robot’s map. Analysis of accuracy, resolution and time of occupancy grid mapping algorithm. Analysis of number of messages, simulation and size of the data generated based on occupancy grid mapping algorithm.
Introduction to Mobile Robot, Motion Planning and Dijkstra's Path Planning Algorithm
Learning overview: Week 11
Introduction to mobile robot, types of mobile robots (Wheeled, Tracked, Legged, AIr-based & Water-based robot), types of wheeled robots and their locomotion (Single wheeled, two-wheeled, three-wheeled, Four-wheeled & five or more wheeled robots), Types of wheels (Standard/Fixed, Orientable Ball and Omni wheel). Introduction to a sensor and its types (Active and Passive). Introduction to an Odometry sensor need, working, and error propagation. Introduction to Motion Planning and its applications. Introduction to path planning, configuration space, obstacle representation, Graph, cost, and graph search. Introduction grid-based path planning, Dijkstra’s algorithm, and pseudo-code of Dijkstra’s algorithm. Programming Dijkstra’s algorithm to find the shortest path in the maze, visualization of the path in python, and results discussion.
Astar Path Planning and Mobile Robot Motion Control in a Maze
Learning overview: Week 12
Introduction Astar algorithm and pseudo code of Astar algorithm. Programming Astar algorithm to find the shortest path in the maze, visualization of the path in python, and results discussion. Introduction to control systems (Open and closed loop), types of controllers in ROS (effort, joint state, Position, Velocity, and Joint Trajectory Controllers), Inertia, Damping, Stiffness, and Mobile Robot description in rviz & gazebo. Introduction to Mobile Robot motion control to control the robot's velocity to control robot’s translation and rotation. Programming motion control algorithm to move the robot to the desired point and Mobile robot motion visualization by representing the maze in gazebo.
Introduction to Sampling Based Path Planning, RRT and RRT* Path Planning Algorithms
Learning overview: Week 13
Introduction to Sampling-based path planning and comparison between grid-based & Sampling-based path planning. Introduction to rapidly exploring random tree (RRT), rapidly exploring random tree star (RRT*) and pseudo code of RRT & RRT*. Programming RRT & RRT* algorithm by representing the environment and cylindrical obstacles. Analysis of RRT algorithm output based on the tree size, no of nodes, and path resolution. Analysis of RRT* algorithm output based on the tree size, no of nodes, maximum iteration, search radius, and path resolution.
Introduction PID Controller and PID based Mobile Robot Motion Control
Learning overview: Week 14
Introduction to Proportional, Integral, Derivative, PI, PD, and PID controllers. Programming Proportional, Integral, Derivative, PI, PD, and PID controllers for motion control of the mobile robot to visualize the robot's motion based on the environment and obstacles represented in gazebo.
Pre-requisites |
None (Course will be taught from fundamentals) ROS & its toolboxes require high computation, it is recommended to have a good hardware configuration (Latest processors, RAM & Graphics card) is necessary. |
Mode of delivery |
Online |
Software used |
Python, ROS, Gazebo & Rviz |
Applicable for |
This course is for engineers who would like to build a career in Robotics, ADAS, Autonomous Systems, Mechatronics & more. |
Session formats |
Enrolled participants will get access to pre-recorded course lectures In decibels LMS. You can learn the courses as per your time planning. |
Doubt clarification |
Addressed during via Discussions option available at each lesson In our LMS. If the query needs a detailed discussion, we will support you through the zoom meeting. |
Course access duration |
5 months (150 days) |
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Micro-Specialisation in Robotics Engineering with Python & ROS
₹34,999.00
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