Revolutionary project alert! 🚀🚗 Utilizing Jupiter Notebook and web development, my app predicts car prices accurately based on vital data like company, model, year, fuel, and kilometers. Empowering car buyers, sellers, and enthusiasts with smart estimates. 💲📊

An advanced lane detection system visualized in a Jupyter Notebook

In this notebook, we're going to discuss the Dubin's Car model and curved flight trajectories as a function of inertia.

In most lessons, thusfar, we've assumed an idealized version of the world and physics. We've assumed that the vehicle always knows where it is in the world as well as knowing where every obstacle is ahead of time. We've even assumed that the vehicle was able to follow a trajectory perfectly through the environment.

Now that we have gone over random sampling in a 2.5D environment, in this notebook we have what we need to construct a 3D, graph-based representation of the feasible parts of the configuration space.

In this notebook, we look into the concept of random sampling and how its implementation is effected by our 2.5D obstacle map.

In this notebook, we look at implementing a 3D Voxel Map of our environment.