This notebook is a continuation of the previous notebook AAE_Notebook_029_3dDrone.

Python notebooks for drone control (CoDrone library)

Now that we've implemented a steer function that, given some start state X1 and some destination state X2, allows us to randomly guess the set of controls that will try to make progress towards X2, we're going to move on an explore Rapidly-Exploring Random Trees (RRTs).

Now that we've begun pruning our path of waypoints, we take a deeper look at collinearity and why it may not be the most optimal of solutions.

This notebook is a continuation of representing orientation of the vehicle based on its Body Frame.

Continuing the theme of multirotor vehicle control, let's start examining the implementation of Closed Loop Controllers.

In this notebook, we glance at one of the major downfalls of a proportional controller and the solution to such a downfall.

In the last notebook, we wrote a method called 'simulate' that allows us to predict where the vehicle will end up given an initial state, some controls, a steering angle, and velocity. Now, let's actually incorporate it into our planner.

This notebook is to further build upon the concepts presented in previous notebooks; more specifically, we're going to test the points within a given path to see if any are collinear.

Now that we've looked at PD Control, let's take things a step further to a full PID Controller implementation...

This notebook is an implementation of a graph-based Voronoi Diagram.

The notebook represents a coordinate conversion from the Geodetic Frame to a N.E.D. aeronautical representation of the E.C.E.F. Frame.

This notebook explores the potential field theory of multirotor control...

This notebook is an implementation of a grids-based medial axis transform.

Now we'll look at a few design considerations for our 3d controller...

In this notebook, we're further extending our controller by implementing a feed-forward term to allow a target acceleration.

Now that we're able to represent the location of the vehicle as a reference point within a coordinate frame (in this case, the Local ECEF Frame) as well as its orientation, thanks to the Body Frame, we can consider motion of the vehicle as a transformation therein.

This Jupyter Notebook forms a simple implementation of the A* Search Algorithm in Python.

Vehicle dynamics are concerned with the motion of bodies under the action of forces. For our purposes, vehicle dynamics references understanding how the rotation of the quadrotor's 4 rotors create forces and how these forces generate motion of the vehicle. In the next few notebooks, we'll learn how to model these motions, mathematically, in Python.

This notebook explains the Body Frame of the vehicle and goes into the usage of Euler Angles and Rotation Matrices as a means by which to represent the vehicle's orientation with the Local ECEF Frame.