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ROS package for using steering angle and collision probability predicted via pre-trained ResNet model for drone navigation using ROS Kinetic in Gazebo 3D environment.

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drone_controls

ROS package for using steering angle and collision probability predicted via pre-trained ResNet model for drone navigation from one point to another using ROS Kinetic in Gazebo 3D environment.

Introduction

The bridge between the drone_controls Keras code and the AR.Drone control is implemented in ROS. Taken inspiration from this repo.

Installation and Setup

Step 1: Install ROS

It is necessary for you to install ROS to have the basic tools available. The project was tested under ROS kinetic, but you can use any other version without problems.

Step 2: Build your workspace

The folder containing all the related code for a project is usually defined as `workspace'. Create your own workspace following these instructions and call it sim_ws.

Step 3: Setup AR.Drone Autonomy

In this step, we will bridge our ROS workspace with the bebop drone. To do it, just follow these instructions.

Be sure to read properly all the instructions about how to run the driver, how to send commands and how to read data from the drone available on the website!

Step 4: Some tests

To make sure that everything is as expected, try to run some tests. Example:

  1. See if you can connect to the drone
  • Open the terminal and run drone.bash.
  1. See if you can receive images from it with rqt_img_view

  2. See if you can publish control commands through the terminal

  3. Try to run the DroNet network:

  • Open the terminal and run run_dronet.sh.

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ROS package for using steering angle and collision probability predicted via pre-trained ResNet model for drone navigation using ROS Kinetic in Gazebo 3D environment.

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