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Self Service Landscape Drones

This project focuses on the development of self-service landscape drones capable of automated terrain mapping and maintenance. These drones are designed to be user-friendly and efficient in performing tasks like mowing, trimming, and landscape surveying.

Table of Contents

Project Overview

The Self Service Landscape Drones project aims to create an automated solution for landscape maintenance. The drones are equipped with advanced sensors and algorithms to navigate and perform landscaping tasks autonomously. The goal is to reduce the need for manual intervention in landscape maintenance, making the process more efficient and less time-consuming.

Features

  • Automated Terrain Mapping: The drones can map the landscape to identify areas that require maintenance.
  • Autonomous Operation: Capable of performing tasks like mowing, trimming, and surveying without manual control.
  • User-Friendly Interface: Easy setup and operation through a user-friendly control system.
  • Obstacle Detection and Avoidance: Equipped with sensors to detect and avoid obstacles during operation.
  • Battery Management: Efficient battery usage and automated charging capabilities.

Prerequisites

  • A computer with an operating system that supports the required technologies (e.g., Python, ROS, etc.).
  • Basic understanding of robotics and drone operation.

Usage

Running the Simulation: Instructions for running the drone simulation or controlling the actual drone will be provided here.

Using the Interface: Details on how to use the control interface for setting up and operating the drones.

Documentation

For detailed documentation, refer to the following files:

Project Definition Document: Contains the project definition, goals, and technical details.

Technologies Used

Hardware: Drone components including sensors, controllers, and power management systems. Software: ROS (Robot Operating System), Python, and other relevant software tools for drone control and operation. Algorithms: Pathfinding, obstacle detection, and terrain mapping algorithms. Contributing Contributions are welcome! Please follow these steps to contribute:

Fork the repository.

Create a new branch for your feature or bug fix. Make your changes and commit them with a descriptive message. Push your changes to the branch and create a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any inquiries or questions, please contact:

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