Quadcopter Exploration

This project is based on the dynamic model and controller designed in this project, which uses PyDy to derive the equations of motion.

This project has as a goal to simulated a quadcopter/quadrotor exploring unknown environments (this is nothing groundbreaking, I'm doing this on my spare time as a hobby and to learn Python).

A pointcloud environment can be generated, where every point of the pointcloud can be considered as an obstacle. Currently, an Artificial Potential Field algorithm is used to navigate through the pointcloud ( each point exerts a force on the drone in order to stop it from colliding with it). More exploration algorithms are to come.

Pointcloud Generation

There are a few available scripts in this branch to generate simple tunnels. They take a while to execute, since a first create a very fine pointcloud, in order to make sure that once voxelized, there are no gaps between points. By voxelized, I mean that I create a corse Cartesian grid (0.25 m between "voxel" centers) and every point of the fine pointcloud is approximated to its closest "voxel".

Artificial Potential Field Implementation

In this Artificial Potential Field, there is no explicit attractive force, as the flight control already commands a velocity, given a desired position. This velocity command is saturated. The velocity control then computes a desired thrust/force, which in turn indicates the desired quadrotor attitude (orientation) (See 'master' readme and PX4 controller)

The repulsive force is calculated using a widely used algorithm (see Siegwart's Introduction to Autonomous Mobile Robots). Every point of the voxelized pointcloud is considered an object. This force is then implemented into the controller through 3 different possible ways.

1. Added to the saturated "attractive" velocity. The resulting desired velocity is then saturated again (to prevent large velocity commands).

2. Added to the desired thrust/force, which is then saturated (to prevent large force commands). The saturation is defined using a specified maximum tilt of the quadrotor.

3. Added to the desired thrust/force after its saturation.

Currently, method # 1 is used, and the repulsive gain is set accordingly.

Useful links

• Youtube video on Artificial Potential Field
• Slides on Artificial Potential Field
• More slides
• A pdf of Siegwart's book can easily be found online ;)

Visualization Using Vispy

Since Matplotlib only uses CPU to compute the drawing, it will not be fast enough to to draw pointclouds of thousands of points. I therefore opted to use the Vispy package for visualization. It uses OpenGL in order to use the power of GPUs.

There are currently 3 different methods to visualize the pointcloud and the points that are withing the Potential Field.

1. Using 2D Markers. This method runs fast but lacks depth perception (markers that are close or far are drawn the same size).

2. Using 3D BoxMarkers. Cubes (boxes) are helpful for depth perception, are visually more appealing but are slightly more GPU intensive.

For now, you can select the visualizing method by commenting and uncommenting lines in the utils\__init__.py file.

Vispy Installation

To be able to visualize the animation, you need to first install Vispy and its dependancies. Vispy requires a OpenGL backend PyQt5, Pyglet or others) (I use PyQt5). You also need PyOpenGL.

If you have the pip package manager installed you can simply type:

$ pip install vispy PyQt5 pyopengl pyopengl_accelerate

OS/GPU settings tweaks

To-Do

• Implement "Extended Potential Field" to reduce oscillations
• Implement "Frontier Based Exploration" (so learn about ray-casting and possibly octomaps)
• Implement some king of "Path Planning"
• Use the multiprocessing Python package to parallelize different tasks (a process for the quadcopter dynamics, one for the control, one for the "Frontier Based Exploration" and one for the visualization)