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germal's Projects

2019-ugrp-dpoom

2019 DGIST DPoom project under UGRP : SBC-Based Full Automation Robot Driving Solution with Indoor SLAM and single RGB-D Camera

3d-astar-thetastar

Basic algorithms for height map based 3D path planning: BFS, Dijkstra, A*, Theta*

3d-point-cloud-analytics

Portfolio for 3D Point Cloud Processing from www.shenlanxueyuan.com China

aae_003_3dquadcontrol

Project 003 of Udacity's Autonomous Aerial Engineer ("Flying Car") Nanodegree, "3D Quadrotor Control"

aae_004_buildinganestimator

Project 004 of Udacity's Autonomous Aerial Engineer ("Flying Car") Nanodegree, "Building An Estimator".

aae_notebook_001_astarsearch

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

aae_notebook_002_geoned

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 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.

aae_notebook_004_quaternions

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

aae_notebook_005_configspace

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.

aae_notebook_006_collinearity

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.

aae_notebook_007_bresenham

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.

aae_notebook_008_putittogether

Thanks to the steps taken in previous notebooks, we now have the needed tools to implement a full planning solution.

aae_notebook_009_medialaxis

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

aae_notebook_010_voronoigraphs

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

aae_notebook_011_voxelmap

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

aae_notebook_012_randomsampling

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

aae_notebook_013_probabilisticroadmap

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.

aae_notebook_015_modeldynamics

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.

aae_notebook_016_dubinscar

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

aae_notebook_017_steering

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.

aae_notebook_018_rrt

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).

aae_notebook_020_potentialfield

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

aae_notebook_021_coaxialdrone

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.

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