Abstract
The “Autonomous Drone Navigation System” project focuses on developing a system that enables drones to navigate autonomously without human intervention. This system employs advanced technologies, such as computer vision, GPS, and artificial intelligence (AI). Specifically, these technologies enable drones to detect and avoid obstacles, follow predefined paths, and adapt to dynamic environments. Therefore, the system provides a robust solution for various applications. The project aims to enhance the capabilities of drones in various applications, including surveillance, search and rescue, agriculture, and logistics, by making them more efficient, reliable, and safe.
Proposed System
The proposed system equips drones with a combination of sensors, cameras, and AI algorithms that work together to achieve autonomous navigation. The drone’s onboard computer processes data from these sensors to create a real-time map of its surroundings, enabling it to detect obstacles and navigate through them. GPS technology is used for precise location tracking. Meanwhile, AI-based path-planning algorithms determine the optimal route for the drone to follow. Furthermore, the system includes a remote monitoring interface that allows users to track the drone’s status and receive alerts in the event of any issues.
Existing System
Traditional drone navigation systems rely heavily on human operators for control, limiting their autonomy and requiring constant supervision. These systems often use basic GPS navigation, which is insufficient for complex environments with obstacles or changing conditions. In cases where drones are used for repetitive tasks, manual control can be inefficient and prone to errors. Additionally, existing systems may lack the ability to dynamically adjust routes based on real-time data, reducing their effectiveness in unpredictable environments.
Methodology
The methodology for the Autonomous Drone Navigation System includes the following steps:
- System Design and Integration: Designing the drone’s hardware and integrating sensors, cameras, and computing units capable of processing real-time data.
- Sensor and Camera Setup: Installing LiDAR, ultrasonic sensors, and cameras to detect obstacles, measure distances, and capture environmental data.
- Data Processing and Mapping: Developing algorithms for real-time data processing and environmental mapping, enabling the drone to understand its surroundings.
- Path Planning and Obstacle Avoidance: Implementing AI-based path planning algorithms to calculate optimal routes and dynamically adjust the drone’s path to avoid obstacles.
- Testing and Simulation: Conducting simulations and real-world tests to validate the drone’s ability to navigate autonomously in various environments.
- User Interface Development: Creating a remote monitoring interface that allows users to track the drone’s status, view real-time data, and receive alerts.
- Deployment and Optimization: Deploying the system in target applications and continuously optimizing it based on performance feedback.
Technologies Used
- Computer Vision: For real-time image processing and environmental mapping.
- LiDAR and Ultrasonic Sensors: For obstacle detection and distance measurement.
- GPS Technology: For precise location tracking and navigation.
- Artificial Intelligence (AI): For path planning, obstacle avoidance, and autonomous decision-making.
- Onboard Computing: For real-time data processing and control of the drone.
- Remote Monitoring Interface: For user interaction, status monitoring, and alert management.
