he research landscape/ topics of Unmanned Air Vehicles (UAV) technology including the charts & narrative below is auto-generated by a machine with Artificial Intelligence (AI). To know about the technology behind this auto-generation, read my previous blog on de-cluttering life (AI to de-clutter life) and for other specific applications see “AI can de-clutter crowd wisdom in online marketplaces”
From this line onward everything is auto-generated by code and AI.
UAV research papers analysis
Machine analyzed 547 papers filed between 2006–09–25 19:06:59 and 2018–09–20 21:23:00
Note: Since 102 days 00:00:00 hours are remaining in this year since last paper filed the charts will show a dip in 2018
15 topics are contained in the repository
List of topics under which papers have been filed in descending order are 1 uav-data-control-using 2 network-uavs-cellular-networks 3 uav-trajectory-problem-convex 4 users-optimal-uavs-coverage 5 control-quadrotor-nonlinear-dynamics 6 coverage-probability-height-user 7 object-detection-image-scene 8 energy-consumption-ers-communication 9 planning-path-inspection-paths 10 drone-drones-delivery-tsp 11 wind-airspeed-flight-strategies 12 tracking-beam-track-analog 13 noma-feedback-transmission-user 14 safety-hj-reachability-guarantees 15 iot-devices-things-internet
How did the topics evolve?
The top 4 topics in which papers was filed were 1 problem-safety-control-uav 2 attitude-quadrotor-tracking-dynamics 3 olsr-networks-routing-hoc 4 rigid-body-links-cable
Today the top 4 topics in which papers being filed are 1 uav-data-control-using 2 network-uavs-cellular-networks 3 uav-trajectory-problem-convex 4 users-optimal-uavs-coverage
Are there key topics back dated in the conversation flow that one should be aware of?
Topic: problem-safety-control-uav — found in chunk 0 / 6 was prominent then but has merged or split later
Topic: uav-algorithm-data-uavs — found in chunk 2 / 6 was prominent then but has merged or split later
Topic: control-uav-algorithm-using — found in chunk 3 / 6 was prominent then but has merged or split later
Let us look at the trends in the conversation
uav-data-control-using — is continuously trending upwards
network-uavs-cellular-networks — is continuously trending upwards
Some topics have significantly changed their rankings
network-uavs-cellular-networks has significantly improved it’s position in the timeframe
uav-trajectory-problem-convex has significantly improved it’s position in the timeframe
users-optimal-uavs-coverage has significantly improved it’s position in the timeframe
coverage-probability-height-user has significantly improved it’s position in the timeframe
Top 5 authors are: 1 Rui Zhang 2 Merouane Debbah 3 Taeyoung Lee 4 Luiz A. DaSilva 5 Roland Siegwart
Top 5 research area are: 1 cs.RO (Robotics) 2 math.IT (Information Theory) 3 cs.IT (Information Theory) 4 cs.SY (Systems and Control) 5 cs.NI (Networking and Internet Architecture)
Let us look at the trends in the research area
Note: The chart shape may change as papers with no corporate assignee is discarded
cs.IT — is continuously trending upwards
cs.RO — is continuously trending upwards
cs.SY — is continuously trending upwards
math.IT — is continuously trending upwards
Refer arXiv.org for abbreviations
Machine gives you a sneak peak of what these 4 topics are:
2 network-uavs-cellular-networks — — — — — — — — — — — — — — — — — — — — — — — — — Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable a reliable wireless connectivity for the UAVs as well as a secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this paper, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, artificial neural network (ANN) based solution schemes are introduced. The introduced approaches enable the UAVs to adaptively exploit the wireless system resources while guaranteeing a secure operation, in real-time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use case.
1 uav-data-control-using — — — — — — — — — — — — — — — — — — — — — — — — — Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of hand-crafted geometric features and the use of external sensors in order to allow the vehicle to approach the land-pad. In this article, we propose a method based on deep reinforcement learning that only requires low-resolution images taken from a down-looking camera in order to identify the position of the marker and land the UAV on it. The proposed approach is based on a hierarchy of Deep Q-Networks (DQNs) used as high-level control policy for the navigation toward the marker. We implemented different technical solutions, such as the combination of vanilla and double DQNs, and a partitioned buffer replay. Using domain randomization we trained the vehicle on uniform textures and we tested it on a large variety of simulated and real-world environments. The overall performance is comparable with a state-of-the-art algorithm and human pilots.
3 uav-trajectory-problem-convex — — — — — — — — — — — — — — — — — — — — — — — — — This paper investigates a new spectrum sharing scenario between unmanned aerial vehicle (UAV) and terrestrial wireless communication systems. We consider that a cognitive/secondary UAV transmitter communicates with a ground secondary receiver (SR), in the presence of a number of primary terrestrial communication links that operate over the same frequency band. We exploit the UAV’s controllable mobility via trajectory design, to improve the cognitive UAV communication performance while controlling the co-channel interference at each of the primary receivers (PRs). In particular, we maximize the average achievable rate from the UAV to the SR over a finite mission/communication period by jointly optimizing the UAV trajectory and transmit power allocation, subject to constraints on the UAV’s maximum speed, initial/final locations, and average transmit power, as well as a set of interference temperature (IT) constraints imposed at each of the PRs for protecting their communications. However, the joint trajectory and power optimization problem is non-convex and thus difficult to be solved optimally. To tackle this problem, we propose an efficient algorithm that ensures to obtain a locally optimal solution by applying the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that our proposed joint UAV trajectory and power control scheme significantly enhances the achievable rate of the cognitive UAV communication system, as compared to benchmark schemes.
4 users-optimal-uavs-coverage — — — — — — — — — — — — — — — — — — — — — — — — — Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. We assume that the locations of indoor users are uniformly distributed in each floor and we propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users.
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