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Robotics Specialization

Learn the Building Blocks for a Career in Robotics. Gain experience programming robots to perform in situations and for use in crisis management

Months Icon 3 Months
38 Modules
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Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

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Physical & Virtual Online Classrooms

Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.

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Technical or Technological, we give you assistance for every challenge you face round-the-clock.

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Job Interview & Assistance

Guiding in & out, until you get placed in your dream job.

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Live projects with our industry partners

An inside look & feel at industry environments by handling real-time projects.

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Internship after course

Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.

Why Robotics ?

Why Data Science
Numerous opportunities available

Robotics is a field with an ever-growing number of job opportunities and facilities provided to those who enter in it. The sector is gaining attention as the days go by and certainly becoming relevant to the public and private sector alike.

Why Data Science
It is a multidisciplinary field

Robotics is a mix of many different fields. You will require to have a knowledge of mechanical engineering, electrical engineering, as well as computer science & cognitive psychology. The field also overlaps largely with artificial intelligence, mechatronics, nanotechnology, and bioengineering.

Why Data Science
It is an extremely creative and interesting field

Robotics is moving forward at light’s speed. This ensures there is room for creativity and critical thinking, out of the box initiatives and extraordinary procedures that will do wonders to keep you engaged for years.

Who is This program for

  • Fresh graduates who wish to make a career in the field of Robotics.
  • Final year engineering students who are interested in Robotic Science
  • People who are curious about how the devices are built and interested in understanding the principles of their operation
  • Best suit for people who would like to become professionals such as Robotics Engineer, RPA Developer, Robotics Application Research Engineer
Who is this program

Syllabus

Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.

How can we create agile micro aerial vehicles that are able to operate autonomously in cluttered indoor and outdoor environments? You will gain an introduction to the mechanics of flight and the design of quadrotor flying robots and will be able to develop dynamic models, derive controllers, and synthesize planners for operating in three dimensional environments. You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments. Finally, you will gain insights through seeing real world examples of the possible applications and challenges for the rapidly-growing drone industry.

Mathematical prerequisites: Students taking this course are expected to have some familiarity with linear algebra, single variable calculus, and differential equations. Programming prerequisites: Some experience programming with MATLAB or Octave is recommended (we will use MATLAB in this course.) MATLAB will require the use of a 64-bit computer.

WEEK 1 : Introduction to Aerial Robotics

  • Unmanned Aerial Vehicles
  • Quadrotors
  • Key Components of Autonomous Flight
  • State Estimation
  • Applications
  • Meet the TAs
  • Basic Mechanics
  • Dynamics and 1-D Linear Control
  • Design Considerations
  • Design Considerations (continued)
  • Agility and Maneuverability
  • Component Selection
  • Effects of Size
  • Supplementary Material: Introduction
  • Supplementary Material: Dynamical Systems
  • Supplementary Material: Rates of Convergence
  • Setting up your Matlab programming environment
  • Matlab Tutorials - Introduction to the Matlab Environment
  • Matlab Tutorials - Programming Basics
  • Matlab Tutorials - Advanced Tools

WEEK 2: Geometry and Mechanics

  • Transformations
  • Rotations
  • Euler Angles
  • Axis/Angle Representations for Rotations
  • Angular Velocity
  • Supplementary Material: Rigid-Body Displacements
  • Supplementary Material: Properties of Functions
  • Supplementary Material: Symbolic Calculations in Matlab
  • Supplementary Material: The atan2 Function
  • Supplementary Material: Eigenvalues and Eigenvectors of Matrices
  • Supplementary Material: Quaternions
  • Supplementary Material: Matrix Derivative
  • Supplementary Material: Skew-Symmetric Matrices and the Hat Operator
  • Formulation
  • Newton-Euler Equations
  • Principal Axes and Principal Moments of Inertia
  • Quadrotor Equations of Motion
  • Supplementary Material: State-Space Form
  • Supplementary Material: Getting Started With the First Programming Assignment

WEEK 3: Planning and Control

  • 2-D Quadrotor Control
  • 3-D Quadrotor Control
  • Time, Motion, and Trajectories
  • Time, Motion, and Trajectories (continued)
  • Motion Planning for Quadrotors
  • Supplementary Material: Minimum Velocity Trajectories from the Euler-Lagrange Equations
  • Supplementary Material: Solving for Coefficients of Minimum Jerk Trajectories
  • Supplementary Material: Minimum Velocity Trajectories
  • Supplementary Material: Linearization of Quadrotor Equations of Motion

WEEK 4: Advanced Topics

  • Sensing and Estimation
  • Nonlinear Control
  • Control of Multiple Robots
  • Adjourn
  • Supplementary Material: Introduction to the Motion Capture System by Matthew Turpin

Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world and a decision and control system which modulates the robot's behavior to achieve the desired ends. In this course we will consider the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations. You will learn some of the most common approaches to addressing this problem including graph-based methods, randomized planners and artificial potential fields. Throughout the course, we will discuss the aspects of the problem that make planning challenging.

WEEK 1: Introduction and Graph-based Plan Methods

  • Introduction to Computational Motion Planning
  • Grassfire Algorithm
  • Dijkstra's Algorithm
  • Algorithm
  • Getting Started with the Programming Assignments
  • Computational Motion Planning Honor Code
  • Getting Started with MATLAB
  • Resources for Computational Motion Planning
  • Graded MATLAB Assignments
  • Graph-based Planning Methods

WEEK 2: Configuration Space

  • Introduction to Configuration Space
  • RR arm
  • Piano Mover’s Problem
  • Visibility Graph
  • Trapezoidal Decomposition
  • Collision Detection and Freespace Sampling Methods
  • Configuration Space

WEEK 3: Sampling-based Planning Methods

  • Introduction to Probabilistic Road Maps
  • Issues with Probabilistic Road Maps
  • Introduction to Rapidly Exploring Random Trees
  • Sampling-based Methods

WEEK 4: Artificial Potential Field Methods

  • Constructing Artificial Potential Fields
  • Issues with Local Minima
  • Generalizing Potential Fields
  • Course Summary

How can robots use their motors and sensors to move around in an unstructured environment? You will understand how to design robot bodies and behaviors that recruit limbs and more general appendages to apply physical forces that confer reliable mobility in a complex and dynamic world. We develop an approach to composing simple dynamical abstractions that partially automate the generation of complicated sensorimotor programs. Specific topics that will be covered include: mobility in animals and robots, kinematics and dynamics of legged machines, and design of dynamical behavior via energy landscapes.

WEEK 1: Introduction: Motivation and Background

  • Why and how do animals move?
  • Bioinspiration
  • Legged Mobility: dynamic motion and the management of energy
  • Review LTI Mechanical Dynamical Systems
  • Introduce Nonlinear Mechanical Dynamical Systems: the dissipative pendulum in gravity
  • Linearization & Normal Forms
  • Setting up your MATLAB environment
  • Getting Started with MATLAB
  • Programming
  • Why and how do animals move
  • Bioinspiration
  • Legged Mobility: dynamic motion and the management of energy
  • Nonlinear mechanical systems
  • Linearizations

WEEK 2: Behavioral (Templates) & Physical (Bodies)

  • Walking like a rimless wheel
  • Running like a spring-loaded pendulum
  • Controlling the spring-loaded inverted pendulum
  • Metrics and Scaling: mass, length, strength
  • Materials, manufacturing, and assembly
  • Design: figures of merit, robustness
  • Actuator technologies
  • Walking like a rimless wheel
  • Running like a spring-loaded pendulum
  • Controlling the spring-loaded inverted pendulum
  • Metrics and Scaling: mass, length, strength
  • Materials, manufacturing, and assembly
  • Design: figures of merit, robustness
  • Actuator technologies

WEEK 3: Anchors: Embodied Behaviors

  • Review of kinematics
  • Introduction to dynamics and control
  • Sprawled posture runners
  • Quadrupeds
  • Bipeds
  • Review of kinematics (MATLAB)
  • Introduction to dynamics and control
  • Sprawled posture runners
  • Quadrupeds
  • Bipeds
  • Simply stabilized SLIP (MATLAB)

WEEK 4: Composition (Programming Work)

  • Sequential and Parallel Composition
  • Why is parallel hard?
  • SLIP as a parallel vertical hopper and rimless wheel
  • RHex: A Simple & Highly Mobile Biologically Inspired Hexapod Runner
  • Clocked RHex gaits
  • Compositions of vertical hoppers
  • Same composition, different bodies
  • Same body, different compositions
  • Transitions: RHex, Jerboa, and Minitaur leaping
  • practice exercises
  • Sequential and Parallel Composition
  • Why is parallel hard?
  • SLIP as a parallel composition
  • Clocked RHex gaits
  • Compositions of vertical hoppers
  • Same composition, different bodies
  • Same body, different compositions
  • Transitions

How can robots perceive the world and their own movements so that they accomplish navigation and manipulation tasks? In this module, we will study how images and videos acquired by cameras mounted on robots are transformed into representations like features and optical flow. Such 2D representations allow us then to extract 3D information about where the camera is and in which direction the robot moves. You will come to understand how grasping objects is facilitated by the computation of 3D posing of objects and navigation can be accomplished by visual odometry and landmark-based localization.

WEEK 1: Geometry of Image Formation

  • Introduction
  • Camera Modeling
  • Single View Geometry
  • More on Perspective Projection
  • Glimpse on Vanishing Points
  • Perspective Projection I
  • Perspective Projection II
  • Point-Line Duality
  • Rotations and Translations
  • Pinhole Camera Model
  • Focal Length and Dolly Zoom Effect
  • Intrinsic Camera Parameter
  • 3D World to First Person Transformation
  • How to Compute Intrinsics from Vanishing Points
  • Camera Calibration
  • Setting up MATLAB
  • Vanishing Points
  • Perspective Projection
  • Rotations and Translations
  • Dolly Zoom
  • Feeling of Camera Motion
  • How to Compute Intrinsics from Vanishing Points
  • Camera Calibration

WEEK2: Projective Transformations

  • Vanishing Points; How to Compute Camera Orientation
  • Compute Projective Transformations
  • Projective Transformations and Vanishing Points
  • Cross Ratios and Single View Metrology
  • Two View Soccer Metrology
  • Homogeneous Coordinates
  • Projective Transformations
  • Vanishing Points
  • Cross Ratios and Single View Metrology

WEEK 3: Pose Estimation

  • Visual Features
  • Singular Value Decomposition
  • RANSAC: Random Sample Consensus
  • Pose from 3D Point Correspondences: The Procrustes Problem
  • Pose from Projective Transformations
  • Pose from Point Correspondences P3P
  • Visual Features
  • Singular Value Decomposition
  • RANSAC
  • 3D-3D Pose
  • Pose Estimation

WEEK 4 : Multi-View Geometry

  • Epipolar Geometry
  • RANSAC: Random Sample Consensus II6m
  • Nonlinear Least Squares I3m
  • Nonlinear Least Squares II6m
  • Nonlinear Least Squares III13m
  • Optical Flow: 2D Point Correspondences
  • 3D Velocities from Optical Flow
  • 3D Motion and Structure from Multiple Views
  • Visual Odometry
  • Bundle Adjustment

How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.

WEEK 1: Gaussian Model Learning

  • 1D Gaussian Distribution
  • Maximum Likelihood Estimate (MLE)
  • Multivariate Gaussian Distribution
  • MLE of Multivariate Gaussian
  • Gaussian Mixture Model (GMM)
  • GMM Parameter Estimation via EM
  • Expectation-Maximization (EM)
  • MATLAB Tutorial - Getting Started with MATLAB
  • Setting Up your MATLAB Environment
  • Basic Probability

WEEK 2: Bayesian Estimation - Target Tracking

  • Kalman Filter Motivation
  • System and Measurement Models
  • Maximum-A-Posterior Estimation
  • Extended Kalman Filter and Unscented Kalman Filter

WEEK 3: Mapping

  • Introduction to Mapping
  • Occupancy Grid Map
  • Log-odd Update
  • Handling Range Sensor
  • Introduction to 3D Mapping

WEEK 4: Bayesian Estimation - Localization

  • Odometry Modeling
  • Map Registration
  • Particle Filter
  • Iterative Closest Point
  • Closing

In our 6 week Robotics Capstone, we will give you a chance to implement a solution for a real world problem based on the content you learnt from the courses in your robotics specialization. It will also give you a chance to use mathematical and programming methods that researchers use in robotics labs.

You will choose from two tracks - In the simulation track, you will use Matlab to simulate a mobile inverted pendulum or MIP. The material required for this capstone track is based on courses in mobility, aerial robotics, and estimation. In the hardware track you will need to purchase and assemble a rover kit, a raspberry pi, a pi camera, and IMU to allow your rover to navigate autonomously through your own environment Hands-on programming experience will demonstrate that you have acquired the foundations of robot movement, planning, and perception, and that you are able to translate them to a variety of practical applications in real world problems. Completion of the capstone will better prepare you to enter the field of Robotics as well as an expansive and growing number of other career paths where robots are changing the landscape of nearly every industry. Please refer to the syllabus below for a week by week breakdown of each track.

WEEK 1: Capstone Introduction and Choosing the Capstone Project

  • Introduction to the Mobile Inverted Pendulum (MIP)
  • Introduction to the Autonomous Rover (AR)
  • Using MATLAB for Dynamic Simulations
  • Dijkstra's Algorithm
  • Purchasing the Robot Kit
  • The Rover Simulator
  • Integrating an ODE with MATLAB

WEEK 2: Newton's Laws; Damped and Undamped

  • PD Control for a Point Particle in Space
  • PD Control for Second-Order Systems
  • Infinitesimal Kinematics; RR Arm
  • Building the Autonomous Rover (AR)
  • Connecting to the Pi
  • Soldering tips
  • Soldering the Motor Hat and IMU
  • Flashing your Raspberry Pi SD Card
  • Assembling the Robot
  • Expanding the SD Card Partition
  • Remote Access to the Pi
  • Controlling the Rover
  • practice exercise
  • PD Tracking

WEEK 3 : Extended Kalman Filter

  • Using an EKF to get Scalar Orientation from an IMU
  • Calibration
  • Camera Calibration
  • Rotations and Translations
  • Camera to body calibration
  • Introduction to Apriltags
  • Motor Calibration
  • Printing your own AprilTags
  • IMU Accelerometer Calibration
  • EKF for Scalar Attitude Estimation
  • Calibration

WEEK 4: Lagrangian Dynamics

  • Modeling a Mobile Inverted Pendulum (MIP)
  • 2-D Quadrotor Control
  • Designing a Controller for the Rover
  • Dynamical simulation of a MIP

WEEK 5: Linearization

  • Local Linearization of a MIP and Linearized Control
  • Kalman Filter Model
  • Extended Kalman Filter Model
  • An Extended Kalman Filter for the Rover
  • Balancing Control of a MIP

WEEK 6: Motion Planning for Quadrotors

  • Feedback Motion Planning for the MIP
  • Integration
450+
Hours of Content
12
Case Study & Projects
35+
Live Sessions
11
Coding Assignments
10
Capstone Projects to Choose From
20
Tools, Languages & Libraries

Languages and Tools covered

Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered

Hands On Projects

In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models

Quadcopter

In this robotics project, we will learn how to build a quadcopter and understand the control mechanism

Gesture Based Robots

Learn to build robots that involve human-machine interaction. Our hand movements can control the robot. An accelerometer sensor controls the robot’s movement which can detect the movement of the human hand along 3 axes.

Voice controlled Robots

Learn to build voice controlled robots. We can use voice recognition technology to give instructions to a robot and control the movement. Wireless or bluetooth connectivity establishes the communication.

Sensor Based Robots

Sensors are one of the major components in robotics projects. In this project, you understand about the integration, calibration and testing of sensors. Here you can use IR sensors to detect the obstacles and program the robot to follow or avoid it.

Swarm Robots

Swarm Robotics deals with Artificial Swarm Intelligence and involves the usage of multiple robots which can coordinate among themselves to complete a mission. In this project, you will develop a master as well as a slave robot that communicates with each other wirelessly.

Autonomous Robots

Autonomous robots can act on their own, independent of any controller. The project is to program the robot to respond to the external stimuli. We use a programmed Arduino board to act as the robot’s brain and process the data coming from the IR sensors for the robot to move along a defined path

Certification

Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

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Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.

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What do you benefit from this programs

You will learn the following skills from this program

  • Motion Planning
  • Particle Filter
  • Matlab
  • Robotics
  • Quadcopter

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