MACHINE LEARNING-BASED AUTONOMOUS DRONE NAVIGATION

Abstract

The present invention relates to a machine learning-based autonomous navigation system for drones, enabling real-time obstacle detection, path optimization, and adaptive decision-making in dynamic environments. The system integrates various onboard sensors such as cameras, LIDAR, radar, and GPS, which provide continuous environmental data that is processed by machine learning algorithms, including deep reinforcement learning. This allows the drone to autonomously navigate complex environments, avoid obstacles, and optimize flight paths for efficiency and safety. The system continuously learns from past experiences to improve navigation performance over time, making it suitable for a wide range of applications such as surveillance, delivery, inspection, and mapping.

Specification

Description:The embodiments of the present invention generally relates to autonomous navigation systems for unmanned aerial vehicles (UAVs), specifically drones, and more particularly to a machine learning-based approach for enabling real-time navigation, obstacle detection, and path optimization. The invention utilizes machine learning algorithms to allow drones to autonomously navigate dynamic environments, adapt to changing conditions, and improve navigation performance over time, making it suitable for a wide range of applications, including surveillance, delivery, mapping, and inspection.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as , Claims:1. A system for autonomous navigation of a drone, comprising:
a drone body with a plurality of sensors including one or more cameras, LIDAR, radar, or GPS for real-time environmental awareness;
a processor configured to receive data from said sensors;
a machine learning model trained to process sensor data and make decisions regarding the navigation of the drone in a dynamic environment;
a flight control system configured to autonomously adjust the drone's path, speed, and trajectory based on the decisions made by the machine learning model.

2. The system of claim 1, wherein the machine learning model comprises a deep reinforcement learning algorithm that optimizes navigation decisions based on historical flight data.

3. The system of claim 1, wherein the flight control system is capable of real-time obstacle detection and avoidance based on the sensor data.

4. The system of claim 1, wherein the machine learning model is continuously updated and improved based on new flight data collected during operation.

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