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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.
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.
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.
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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
WEEK 2: Geometry and Mechanics
WEEK 3: Planning and Control
WEEK 4: Advanced Topics
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
WEEK 2: Configuration Space
WEEK 3: Sampling-based Planning Methods
WEEK 4: Artificial Potential Field Methods
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
WEEK 2: Behavioral (Templates) & Physical (Bodies)
WEEK 3: Anchors: Embodied Behaviors
WEEK 4: Composition (Programming Work)
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
WEEK2: Projective Transformations
WEEK 3: Pose Estimation
WEEK 4 : Multi-View Geometry
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
WEEK 2: Bayesian Estimation - Target Tracking
WEEK 3: Mapping
WEEK 4: Bayesian Estimation - Localization
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
WEEK 2: Newton's Laws; Damped and Undamped
WEEK 3 : Extended Kalman Filter
WEEK 4: Lagrangian Dynamics
WEEK 5: Linearization
WEEK 6: Motion Planning for Quadrotors
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models
In this robotics project, we will learn how to build a quadcopter and understand the control mechanism
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.
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.
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 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 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
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 positionEnroll Now
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Artificial Intelligence which is a global company with headquarters in Chicago, USA. Artificial Intelligence has partnered with GamaSec, a leading Cyber Security product company. Artificial Intelligence is focusing on building Cyber Security awareness and skills in India as it has a good demand in consulting and product support areas. The demand for which is predicted to grow exponentially in the next 3 years. The Artificial Intelligence training programs are conducted by individuals who have in depth domain experience. These training sessions will equip you with the fundamentalknowledge and skills required to be a professional cyber security consultant.
All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.
There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.
You can get started by enrolling yourself. The enrollment can be initiated from this website by clicking on "ENROLL NOW". If you are having questions or difficulties regarding this, you can talk to our counselors and they can help you with the same.
Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.
You will learn the following skills from this program
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