Cellular for the skies: exploiting mobile network infrastructure for low altitude air-to-ground communications

In this article we presented an overview of UASs for civil applications focusing on the communication component. We identified several available communication technologies for UAVs, their constraints, and also protocols available for implementing the remote operation of the vehicles. As an attractive solution for the A2G communication link for UAVs, we discussed the potential of mobile networks with their fully deployed infrastructures, wide radio coverage, high throughputs, reduced latencies, and large availability of radio modems. We described how a UAS can be implemented in a flexible and modular approach that allows it to rely on one or several wireless (UAVs and GCSs) and wired (GCSs) technologies. Despite the advantages of a system based on cellular and IP networks, there are problems that must be dealt with, namely, possible loss of radio coverage, presence of NAT, delay, jitter, and packet loss. Following the proposed architecture, we implemented an UAS and conducted some flight tests, which showed that the operation of the vehicles in semi-automatic or fully-automatic modes is feasible. It is expected that future enhancements for 4G networks and evolution to 5G will benefit UAV communications even further with lower latencies, higher throughput, and higher reliability.info:eu-repo/semantics/acceptedVersio

Location prediction and trajectory optimization in multi-UAV application missions

Unmanned aerial vehicles (a.k.a. drones) have a wide range of applications in e.g., aerial surveillance, mapping, imaging, monitoring, maritime operations, parcel delivery, and disaster response management. Their operations require reliable networking environments and location-based services in air-to-air links with cooperative drones, or air-to-ground links in concert with ground control stations. When equipped with high-resolution video cameras or sensors to gain environmental situation awareness through object detection/tracking, precise location predictions of individual or groups of drones at any instant possible is critical for continuous guidance. The location predictions then can be used in trajectory optimization for achieving efficient operations (i.e., through effective resource utilization in terms of energy or network bandwidth consumption) and safe operations (i.e., through avoidance of obstacles or sudden landing) within application missions. In this thesis, we explain a diverse set of techniques involved in drone location prediction, position and velocity estimation and trajectory optimization involving: (i) Kalman Filtering techniques, and (ii) Machine Learning models such as reinforcement learning and deep-reinforcement learning. These techniques facilitate the drones to follow intelligent paths and establish optimal trajectories while carrying out successful application missions under given resource and network constraints. We detail the techniques using two scenarios. The first scenario involves location prediction based intelligent packet transfer between drones in a disaster response scenario using the various Kalman Filtering techniques. The second scenario involves a learning-based trajectory optimization that uses various reinforcement learning models for maintaining high video resolution and effective network performance in a civil application scenario such as aerial monitoring of persons/objects. We conclude with a list of open challenges and future works for intelligent path planning of drones using location prediction and trajectory optimization techniques.Includes bibliographical references