Dreaming Falcon

Advanced Drone Control Using DREAMER Algorithm

Research Project Machine Learning Autonomous Systems

Project Overview

Developing an advanced drone control system utilizing model-based reinforcement learning to create a self-optimizing flight controller. The system continuously learns from motor outputs and sensor feedback to improve flight performance and adapt to changing conditions in real-time.

Technical Implementation

DREAMER Algorithm Integration

Implemented a sophisticated control system that fuses sensor data with motor outputs for optimized performance.

  • Real-time sensor fusion from multiple input sources
  • Adaptive motor output optimization
  • Environmental condition compensation
  • Dynamic performance adjustment

Sensor Fusion System

Developed comprehensive sensor integration for enhanced environmental awareness.

  • IMU data processing for attitude estimation
  • GPS integration for position tracking
  • Barometric pressure for altitude control
  • Motor telemetry for performance monitoring

Adaptive Control Framework

Created a dynamic control system that learns and adapts to changing conditions.

  • Real-time performance optimization
  • Environmental disturbance rejection
  • Autonomous parameter tuning
  • Fault detection and compensation

Technologies Used

Python TensorFlow ROS MATLAB Simulink PX4 Sensor Fusion

Key Features

  • • Real-time adaptation to environmental changes
  • • Advanced sensor fusion implementation
  • • Autonomous performance optimization
  • • Dynamic motor control adjustment
  • • Robust disturbance rejection