You might want to answer the following questions before pursuing to learn ROBOTICS
- Are you curious about the working of Robots?
- Are you interested in robotics Career?
- Are you willing to learn Core Concepts that are used in all the areas of robotics?
- Are you willing to learn Mathematical Modeling Techniques using in the specific areas of Robotics?
- Are you willing to learn to Code in Python, C or C++?
- Are you willing to commit to 6 months – 1 year of learning to Robotics?
- Are you committed and dedicated to learn ROBOTICS?
If answer to all of above is Yes- then continue reading below and click on the Courses for more details and/or to register:
ROBOTICS Courses offered by University of Pennsylvania for Free- Audit Tracks
You will learn robot configurations, for both serial robot mechanisms and robots with closed chains. Also you will learn about configuration space (C-space), degrees of freedom, C-space topology, implicit and explicit representations of configurations, and holonomic and nonholonomic constraints. Further, you will also learn how to represent spatial velocities and forces as twists and wrenches. This material is at the core of the study of anything that moves (e.g., robots).
In this course, you will learn to solve the forward kinematics (calculating the configuration of the “hand” of the robot based on the joint values) using the product-of-exponentials formula.
In Course 3 – Robot Dynamics, you will learn efficient numerical algorithms for forward dynamics (calculating the robot’s acceleration given its configuration, velocity, and joint forces and torques) and inverse dynamics (calculating the required joint forces and torques given the robot’s configuration, velocity, and acceleration). The former is useful for simulation, and the latter is useful for robot control. You will also learn how to plan robot trajectories subject to dynamic constraints
You will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion.
In Course 5 of the specialisation, Robot Motion Planning and Wheeled Mobile Robots, we delve into advanced topics in robotics. Chapter 12, Grasping and Manipulation, of the “Modern Robotics” textbook covers the Modeling of kinematics and forces between rigid bodies in contact, and applies the Modeling to analysis and planning of robot grasping and other manipulation tasks. Chapter 13, Wheeled Mobile Robots, covers Modeling, motion planning, and feedback control of omnidirectional and nonholonomic wheeled mobile robots, and concludes by addressing control of mobile manipulators consisting of a wheeled mobile base and a robot arm
The capstone project of the Modern Robotics specialisation is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialisation, including trajectory planning, Odometry for mobile robots, and feedback control. You will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
In this course you will learn-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.
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 behaviour 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. You will learn some of the most common approaches to addressing this problem including graph-based methods, randomised planners and artificial potential fields.