Using AI to Improve Autonomous Unmanned Aerial Vehicle Navigation
Keywords:
Unmanned aerial vehicles (UAV), Autonomous navigation, Model-based learning, Sensor integrationAbstract
Unmanned aerial vehicles (UAVs) have become increasingly popular in recent years due to their ability to integrate a wide variety of sensors with minimal disruption, all while maintaining low cost, simple deployment, and unparalleled mobility. However, UAVs' effectiveness is sometimes hampered because of the constraints imposed by remote piloting in complex terrain. As a result, an ever-expanding group of researchers has been hard at work creating autonomous UAV navigation systems, giving these airborne wonders the capacity to travel and carry out tasks based on their immediate context. In this ever-changing context, Artificial Intelligence (AI) has proven pivotal by allowing human-like control functions to be infused into autonomous UAVs. So, a group of forward-thinking scientists has adopted several AI technologies to improve the effectiveness of UAV autonomous navigation, with model-based learning and mathematical-based optimization emerging as two cornerstone AI methodologies. This discussion expands on the complex interplay between AI and UAVs by defining the many characteristics and classes of UAVs, explaining the navigation models they use, and elaborating on the wide range of tasks they may do. In turn, this should help people grasp how important AI is in expanding the scope and potential of unmanned aerial vehicles. There are no limits to what may be accomplished in the sky thanks to the rapid development of unmanned aerial vehicle (UAV) technology and the convergence of artificial intelligence.
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