An Efficient and Rapidly Adaptable Lightweight Multi-Destination Urban Path Planning Approach for UAVs using Q-Learning

Advancement in UAV technologies have facilitated the development of lightweight airborne platforms capable of fulfilling a diverse range of tasks due to a varied array of mountable sensing and interaction modules available. To further advance UAVs and widen their application spectrum, providing them with fully autonomous operations capability is necessary. To address this challenge, we present Multiple Q-table Path Planning (MQTPP), a novel method specifically tailored for UAV path planning in urban environments. Unlike a conventional Q-learning approach that necessitates relearning in response to dynamic changes in urban landscapes or targets, MQTPP is designed to adaptively re-plan UAV paths with notable efficiency, utilising a singular learning phase executed prior to take-off. Results obtained through simulation demonstrate the exceptional capability of MQTPP to swiftly generate new paths or modify existing ones during flight. This performance significantly surpasses existing state-of-the-art methods in terms of computational efficiency, while still achieving near-optimal path planning results. Thus, demonstrating MQTPP's potential as a robust solution for real-time, adaptive in-flight UAV navigation in complex urban settings.