Robotics

Here’s the content from the Robotics Specialization: Estimation and Learning offered by UPenn on Coursera. This is the fifth course in the robotics specialization. This course is about topics related to machine learning and estimation in robotics. Gaussian Model Learning 1D Gaussian Distribution The Gaussian distribution is a widely used probability distribution defined by two parameters, the mean (μ) and standard deviation (σ), which represent its central value and spread. It is characterized by a bell-shaped probability density function (PDF) and has some nice mathematical properties such as the product of two Gaussian distributions is also Gaussian. Additionally, the Central Limit Theorem states that the sum or average of a large number of independent and identically distributed random variables converges to a Gaussian distribution, making it suitable for modeling noise in measurement or uncertainty. ...

Here’s the content from the Robotics Specialization: Perception offered by UPenn on Coursera. This is the fourth course in the robotics specialization. Throughout this course, we learn the basics of computer vision that involve robotics. Single-view Geometry Pinhole Camera Pinhole camera The pinhole camera is the most commonly used camera model for computer vision. It can be seen as a simple form of perspective projection as shown in the figure below, which refers to the way objects in a three-dimensional scene appear on a two-dimensional surface when viewed from a particular point or viewpoint. The image below shows a 2D example. The rays from an object converge to some points in the image plane. Here $Y$ is the height of the object in the scene and $y$ is the height of the object in the image plane, $Z$ is the distance of the object from the camera, and $f$ is the focal length. ...

Here are the assignments for the Robotics Specialization: Computational Motion Planning offered by UPenn on Coursera. This is the second course in the robotics specialization. Throughout this course, we have gained knowledge on different techniques for planning robot motions. These include graph-based methods, randomized planners, and artificial potential fields. Graph-based Methods Graph-based methods tackle the challenge of planning routes for robots in environments with discrete positions, such as grids. To address this problem, we can represent such scenarios as graphs, where nodes signify grid locations, and edges indicate the routes between neighboring grid cells. In this course, we learned and implemented two graph-based algorithms, Dijkstra and the A-Star algorithm. ...

Here are the assignments for the Robotics Specialization: Aerial Robotics offered by UPenn on Coursera. In this course, we learned some basic ideas about autonomous robots and the design of quadrotors. There are three assignments in this course in which we learned to design a PD controller for a quadrotor in 1D, 2D, and 3D. The course provided quadrotor simulators to help with the experiment. The simulator uses MATLAB’s ODE solver, ode45, to simulate the behavior of the quadrotor and plt3 to visualize the state of the quadrotor at each time step. ...