Degree
Master of Science in Computer Science
Department
Department of Computer Science
Faculty / School
Faculty of Computer Sciences (FCS)
Date of Submission
2019-12-31
Supervisor
Dr. Muhammad Sarim, Visiting Faculty, Department of Computer Science
Document type
MSCS Survey Report
Keywords
Deep Learning, CNN(Convolution Neural Networks), Aerial Robotics, AAV(Autonomus Aerial Vehicles), Quadrotors, ANN(Artificial Neural Network)
Abstract
In recent years, there has been a wide spread use of miniature drones, also called unmanned aerial vehicles (UAVS) in different civilian applications. Few of the wide range use of these aerial robots include, but are not limited to, search & rescue, covering sports events, helping wildlife conservation efforts (Price, et al. 2018), aerial surveillance, goods transportation, surveying & inspection tasks (Li, et al. 2017), three dimensional mapping, monitoring & irrigating crops (Trujillano, et al. 2018), traffic monitoring etc.
Deep learning algorithms such as deep convolution neural networks or neural networks in general, due to being universal function approximators, have gained the attention of the aerial robotics research community for the last few years. Successfully applying the deep learning algorithms can accomplish the communities’ dream of automating the aforementioned tasks, which until few years back could only be done through a manual control of these drones.
The study is based on the research survey about deep learning applications in aerial robotics, how & what different algorithms can be used in the scenario, what are the specific challenges & solutions facing the implementation of autonomy in these systems. The study focuses on small light weight drones such as Quadrotors, which due to its constrained and limited resources, as compared to large military drones, require efficient algorithms to achieve automation tasks in indoor, outdoor settings.
Recommended Citation
Akbani, M. F. (2019). Deep learning applications in aerial robotics (Unpublished MSCS survey report). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/survey-reports-mscs/56
The full text of this document is only accessible to authorized users.
