Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—Recently, autonomous exploration using robots has been researched and developed for different objectives and requirements. The advancement in image processing using deep learning has made some remarkable results in controlling UAV autonomously in the situation without GPS information. However, there are few types of research on autonomous image-based exploration, especially in the situation that requires the ability to recognize and predict multiple directions from images, which is an important key to perform pathfinding in an exploration mission correctly. In this paper, we propose an approach for this problem by applying a supervised-learning method to predict possible directions from images. We introduce a deep learning architecture using the transfer learning technique to evaluate our dataset. The experiment results show the promising capability of the model for handling situations with multiple directions.
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