phenix2020's repositories
scipy-lecture-notes
Tutorial material on the scientific Python ecosystem
mrasl_guidance_ros
An (incomplete) attempt at making a better DJI Guidance ROS package.
model_for_bar_lifting
Ref_EngageProject
gazebo_ros_demos
Example robots and code for interfacing Gazebo with ROS
argos3
A parallel, multi-engine simulator for heterogeneous swarm robotics
argos3-examples
Examples for ARGoS3
qgroundcontrol
Cross-platform ground control station for drones (Android, iOS, Mac OS, Linux, Windows)
BittyBuzz
BittyBuzz is an implementation of Buzz for microcrontrollers.
ch-1-3
Project files for MBZIRC2017 Challenge 1 and 3
hector_quadrotor
hector_quadrotor contains packages related to modeling, control and simulation of quadrotor UAV systems.
Visual-tracking-from-a-Drone
Code for visual tracking of objects on ground from a drone.
leetcode
LeetCode题解，151道题完整版
BuzzKH4
Khepera IV integration for Buzz
collaborative_load_lifting
Engage Project:
git
Git Source Code Mirror - This is a publish-only repository and all pull requests are ignored. Please follow Documentation/SubmittingPatches procedure for any of your improvements.
rrt
C++ RRT (Rapidly-exploring Random Tree) implementation
Firmware
PX4 Pro Autopilot Software
XX-Net
a web proxy tool
Guidance-SDK
The official Guidance SDK package for Windows, Ubuntu and XU3.
cvg_ardrone2_ibvs
We present a vision based control strategy for tracking and following objects using an Unmanned Aerial Vehicle. We have developed an image based visual servoing method that uses only a forward looking camera for tracking and following objects from a multi-rotor UAV, without any dependence on GPS systems. Our proposed method tracks a user specified object continuously while maintaining a fixed distance from the object and also simultaneously keeping it in the center of the image plane. The algorithm is validated using a Parrot AR Drone 2.0 in outdoor conditions while tracking and following people, occlusions and also fast moving objects; showing the robustness of the proposed systems against perturbations and illumination changes. Our experiments show that the system is able to track a great variety of objects present in suburban areas, among others: people, windows, AC machines, cars and plants. urls: http://www.vision4uav.eu/?q=following and http://robotics.asu.edu/ardrone2_ibvs/ .