Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed.
Aerial filming is becoming more and more popular thanks to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and scientific challenges. In this work, we propose an intelligent agent which supervises motion planning of a filming drone based on aesthetical values of video shots using deep reinforcement learning. Unlike the current state-of-the-art approaches which mostly require explicit guidance by a human expert, our drone learns how to make favorable shot type selections by experience. We propose a learning scheme which exploits aesthetical features of retrospective shots in order to extract a desirable policy for better prospective shots. We train our agent in realistic AirSim simulations using both hand-crafted and human reward functions. We deploy the same agent on a real DJI M210 drone in order to test generalization capability of our approach to real world conditions. To evaluate the success of our approach in the end, we conduct a comprehensive user study in which participants rate the shots taken using our method and write comments about them.
Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.
Observation planning for Unmanned Aerial Vehicles (UAVs) is a challenging task as it requires planning trajectories over a large continuous space and with motion models that can not be directly encoded into current planners. Furthermore, realistic problems often require complex objective functions that complicate problem decomposition. In this paper, we propose a local search approach to plan the trajectories of a fleet of UAVs on an observation mission. The strength of the approach lies in its loose coupling with domain specific requirements such as the UAV model or the objective function that are both used as black boxes. Furthermore, the Variable Neighborhood Search (VNS) procedure considered facilitates the adaptation of the algorithm to specific requirements through the addition of new neighborhoods. We demonstrate the feasibility and convenience of the method on a large joint observation task in which a fleet of fixed-wing UAVs maps wildfires over areas of a hundred square kilometers. The approach allows generating plans over tens of minutes for a handful of UAVs in matter of seconds, even when considering very short primitive maneuvers.
Launching at Singapore port's Marina South Pier in quarter three 2018, Wilhelmsen Ships Service and Airbus will be piloting the delivery of spare parts, documents, water test kits and 3D printed consumables via Airbus' Skyways unmanned air system (UAS) to vessels at anchorage. With the signing of an MOU at maritime trade show Posidonia, the Maritime UAS project agreement covers a joint ambition to establish a framework for cooperation between the Parties, with the aim of investigating the potential deployment and commercialization of UAS for maritime deliveries use cases. Marking the very first time, the viability of autonomous drone delivery to vessels has been put to the test in hectic, real-world port conditions, Marius Johansen, VP Commercial, Ships Agency at Wilhelmsen Ships Service is confident with Airbus now onboard his agency team's long-term drone delivery aspirations will be fulfilled. "We are absolutely thrilled to be working with a forward thinking, industry leader like Airbus. When we announced last year that we were pursuing drone delivery, we were greeted with a fair amount of scepticism, but our collaboration with Airbus, shows we really do mean business".
Technologies like artificial intelligence and deep learning are driving the evolution of drones and fueling their autonomous future, according to Jesse Clayton, senior manager of product management for intelligent machines at Nvidia. Clayton spoke with SearchCIO at the recent InterDrone conference in Las Vegas, where he discussed the underlying technologies that are shaping the future of drones. In this video, he gives an overview of the commercial applications of drones and explains how advances in AI are impacting the drone industry. What are some of the most surprising business applications of drones that you have seen? Jesse Clayton: Before we talk about the applications, it's important to understand some of the big trends that are happening in technology right now.
More and more companies are putting drones to work, including tech giants, manufacturers, utilities, and news organizations. With a broad range of practical applications and rapidly evolving technology, drones offer huge untapped potential, but not every market offers equal opportunities for growth. Here are seven facts and forecasts to know before investing. The demand for drones in the U.S. is projected to rise 10% annually to $4.4 billion in 2020, and the number of vehicles sold will more than double to 5.5 million. Drones sold to commercial and consumer users can cost less than $100 on the low end for toy drones to $10,000 or more on the high end for professional drones with sophisticated sensors and controls.
Despite recent advances in the visual tracking community, most studies so far have focused on the observation model. As another important component in the tracking system, the motion model is much less well-explored especially for some extreme scenarios. In this paper, we consider one such scenario in which the camera is mounted on an unmanned aerial vehicle (UAV) or drone. We build a benchmark dataset of high diversity, consisting of 70 videos captured by drone cameras. To address the challenging issue of severe camera motion, we devise simple baselines to model the camera motion by geometric transformation based on background feature points. An extensive comparison of recent state-of-the-art trackers and their motion model variants on our drone tracking dataset validates both the necessity of the dataset and the effectiveness of the proposed methods. Our aim for this work is to lay the foundation for further research in the UAV tracking area.
When you think of the rapid evolution of technology, the first thing that comes to mind is likely self-driving cars or artificial intelligence, not the real estate industry. But just because the real estate industry is not at the forefront of the technological revolution, it doesn't mean there aren't exciting new developments happening in the sector – and some of them can benefit you as a real estate investor. Nearly every industry has benefited from the advent of "big data," but what does that really mean for real estate? Together, these factors mean we're now able to access and analyze higher volumes of data more quickly. As a result, real estate data companies can now deliver more insightful information to the investment community faster, allowing investors to make better decisions.
Video Friday is your weekly selection of awesome robot videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Opportunity is 13 years old! The hydraulic high-power muscle has been developed by Suzumori Endo Robotics Laboratory at Tokyo Institute of Technology and Bridgestone Corporation as part of the Impulsing PAradigm Change through disruptive Technologies program (ImPACT) Tough Robotics Challenge which is an initiative of the Cabinet Office Council for Science, Technology and Innovation.