<![CDATA[The entry of Autonomous Aerial Vehicles (AAVs) has reshaped multiple industries through novel solutions such as transport, monitoring, and deliveries. Nevertheless, the existence of dynamic operating environments, and the unpredictability of barrier emergence, constitutes a complicated path planning challenge that is difficult to cope with. Current methods of dynamic obstacle avoidance, e.g. Recurrent Neural Networks (RNNs) and Long Short-term Memory (LSTM) networks, accomplished the task and became the essential part of AAV navigation systems development. These techniques may work, but they have a disadvantage of being slow in processing and less energy efficient, which are important for a real-time operation and for a mission which lasts for a long time. The purpose of the research is to fill up the identified gaps by introducing a GRU-based predictive model for dynamic obstacle avoidance in AAV’s. While the previous models concentrate on the improvement of reaction time and energy consumption without the degradation of computational efficiency, the recent GRU model is particularly designed for such purpose. It is realized through a streamlined design that facilitates rapid and precise object trajectory predictions, thus, making AAVs be able to rethink their paths in advance of any obstacles lurking. We show that the RNN-based GRU model is benchmarked significantly better than the RNN and LSTM models in simulated settings. In the Eco mode, the model GRU responded in 0.35 seconds in low-speed and its energy consumption never exceeded 130 units even in the high-speed scenarios with maximum load. Path efficiency was preserved and the path length was kept to the minimum in most cases, which indicates the model's capability in finding the most direct paths. Additionally, computer loads were at a tolerable level, thus further showing the applicability of this model for systems on-board having inducted limits for their processing capabilities. GRU- based model comes out as a robust and economical technique for the obstacle avoidance, giving a potential solution to the critical problems of AAVs.]]>
