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The project presents a drone obstacle avoidance system using Microsoft AirSim and the DDPG algorithm, training drones with LIDAR and depth sensors for improved real-time navigation. It compares the implementation of DDPG algorithm with different sensors and their combination.
Autonomous drone system utilizing image analysis and dynamic movement adjustments for smoke plume tracking and particle sampling : GAIA-drone-control Project
It is intended to be trained using Reinforcement Learning algorithms that aim to ensure drone stabilization in realistic physical conditions, using the AirSim plug-in on the Unreal Engine platform.
This project develops code for autonomous drone navigation in urban areas. Using trajectory planning, the drone maintains height while navigating from start to goal. It detects obstacles with LIDAR and evades them to reach the goal, entering hovering mode upon arrival.