Abstract
Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.
Highlights
IntroductionWe live in the ‘era’ of Unmanned Aircraft Systems (UAS), an all-encompassing term that includes the aircraft or the Unmanned Aerial Vehicles (UAVs), the ground-based controller (the person or software agent operating the machine), and the communications systems connecting the two [1]
We live in the ‘era’ of Unmanned Aircraft Systems (UAS), an all-encompassing term that includes the aircraft or the Unmanned Aerial Vehicles (UAVs), the ground-based controller, and the communications systems connecting the two [1]
The MNV2 model was obtained using Tensorflow by using the transfer learning approach [70], with the weights pre-trained on the ImageNet dataset, which is a large dataset of 1.4 M images and 1000 classes of web images [71]
Summary
We live in the ‘era’ of Unmanned Aircraft Systems (UAS), an all-encompassing term that includes the aircraft or the UAV, the ground-based controller (the person or software agent operating the machine), and the communications systems connecting the two [1]. This level of safety and reliability must be assured regardless of the operation conditions and occurrence of unexpected events This leads to the obvious conclusion that, just as for other autonomous vehicles, it is required to develop a collision avoidance architecture that is agnostic of the environment constrains, and is capable of finding solutions to unexpected events in real-time.
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