Peer Graded Assignment: B5.2 An Extended Kalman Filter for State Estimation ![]() MIP Track: Local linearization of a MIP and linearized controlĪR Track: An Extended Kalman Filter for State Estimation Peer Graded Assignment: B4.2 Programming a Tag Following Algorithm MIP Track: Modeling a Mobile Inverted Pendulum (MIP)ĪR Track: Designing a Controller for the Rover Quiz: A3.2 EKF for Scalar Attitude Estimation MIP Track: Using an EKF to get scalar orientation from an IMU Quiz: B2.10 Demonstrating your Completed Rover MIP Track: PD Control for Second-Order Systems Programming Assignment: B1.3 Dijkstra's Algorithm in Python Quiz: A1.2 Integrating an ODE with MATLAB MIP Track: Using MATLAB for Dynamic SimulationsĪR Track: Dijkstra's and Purchasing the Kit Please refer to the syllabus below for a week by week breakdown of each track. 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. 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. 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 The material required for this capstone track is based on courses in mobility, aerial robotics, and estimation. You will choose from two tracks - In the simulation track, you will use Matlab to simulate a mobile inverted pendulum or MIP. ![]() It will also give you a chance to use mathematical and programming methods that researchers use in robotics labs. ![]() 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. 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. Mathematical prerequisites: Students taking this course are expected to have some familiarity with linear algebra, single variable calculus, and differential equations. Finally, you will gain insights through seeing real world examples of the possible applications and challenges for the rapidly-growing drone industry. You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments. 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.
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