Integrated manufacturing of REciclable multi-material COmposites for the TRANSport sector
Aerospace, Vol. 11, Pages 870: Hybrid Machine Learning and Reinforcement Learning Framework for Adaptive UAV Obstacle Avoidance
This review explores the integration of machine learning (ML) and reinforcement learning (RL) techniques in enhancing the navigation and obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs). Various RL algorithms are assessed for their effectiveness in teaching UAVs autonomous navigation, with a focus on state representation from UAV sensors and real-time environmental interaction. The review identifies the strengths and limitations of current methodologies and highlights gaps in the literature, proposing future research directions to advance UAV technology. Interdisciplinary approaches combining robotics, AI, and aeronautics are suggested to improve UAV performance in complex environments.
» Publication Date: 24/10/2024
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737
