An Statistical Analysis of Gated Recurrent Unit Based Predictive Modelling for Dynamic Obstacle Avoidance in Autonomous Aerial Vehicles

Abstract

More and more, autonomous aerial vehicles (AAVs) are being used for a wide range of tasks, such as monitoring, search and rescue, and item delivery. One important part of AAVs' liberty is that they can safely move through changing surroundings. To be successful at dynamic obstacle avoidance, you need to be able to guess how objects will move in real time using good predictive modeling. In this work, we suggest a new way to use Gated Recurrent Units (GRUs) for predictive models in AAVs' dynamic obstacle avoidance. This is a type of recurrent neural network (RNN) called the GRU. It works well for handling linear data and has shown promise in many areas, such as natural language processing and time series prediction. Through our method, we use GRUs to predict how dynamic objects move by looking at past data. The model projects where the obstacles will be in the future based on where the AAV is now and where they have been in the past. The AAV can change its direction to avoid hitting things by constantly changing its predictions in real time. We use a collection of synthetic AAV flights in changing settings to train the GRU model. The file has details about the AAV's location, speed, and direction, as well as the locations of moving objects. We preprocess the data to get the important traits out of it and make it more uniform so that the training process works better. Then, we train the GRU model using both past data and real-world information about where obstacles will be in the future. We use a set of measures, such as impact rate, forecast accuracy, and processing speed, to judge how well our method works. The outcomes show that the GRU-based predictive modeling method greatly enhances dynamic obstacle avoidance performance when compared to conventional approaches. The AAV that has our model can successfully move through complex settings with changing objects, staying on a smooth path without running into any problems.