Autonomous Unmanned Aerial Vehicles Filming In Dynamic Unstructured Outdoor Environments [Applications Corner]

Mademlis, Ioannis; Nikolaidis, Nikos; Tefas, Anastasios; Pitas, Ioannis; Wagner, Tilman; Messina, Alberto

doi:10.1109/msp.2018.2875190

Cited by 42 publications

(19 citation statements)

References 10 publications

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“…Autonomous deliveries using UAVs have launched in some cities, and it won't be long before UAV drop-offs will be a regular occurrence [52]. The autonomous UAVs, namely DJI Phantom IV Pro (using the Intel Movidius Myriad 2 vision processing unit) and Skydio R1 (built around the more powerful NVIDIA Tegra X1 system-on-a-chip) are being deployed for autonomous videography/filming purposes [53]. An autonomous fixed-wing UAV, so-called, Zipline that can be classified as Level-4 task-level autonomy has been saving lives in Rwanda and Tanzania by delivering critical medical supplies at night, through heavy rain, or in high winds from a central distribution centre to hospitals across the country in minutes within 75 km with a mission-level autonomy where the other transportation modes seem to be far from feasible with bad road infrastructures along with the adverse weather conditions [11].…”

Section: Related Workmentioning

confidence: 99%

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

Kuru

2021

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

Kuru

2021

IEEE Access

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“…Autonomous aerial filming using drones today relies on 3 key groups of technology namely 2-D, 3-D, and Video Capture and Communication to control and plan their flight as highlighted by Mademlis et al [29]. We took reference from the study [29] to decide on which setting is more suitable for our proposed system (i.e. choosing between 2-D and 3-D setting for the visual target detection/tracking subsystem).…”

Section: Deep Learning Approaches For Autonomous Flightsmentioning

confidence: 99%

Aerial filming with synchronized drones using reinforcement learning

Goh

Wong

et al. 2021

Multimed Tools Appl

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Usage of multiple drones is necessary for aerial filming applications to ensure redundancy. However, this could inevitably contribute to higher risks of collisions, especially when the number of drones increases. Hence, this motivates us to explore various autonomous flight formation control methods that have the potential to enable multiple drones to effectively track a specific target at the same time. In this paper, we designed a model-free deep reinforcement learning algorithm, which is mainly based on the Deep Recurrent Q-Network concept, for the aforementioned purposes. The proposed algorithm was expanded into single and multi-agent types that enable multiple drones tracking while maintaining formation and preventing collision. The involved rewards in these approaches are two-dimensional in nature and are dependent on the communication system. Using Microsoft AirSim simulator, a virtual environment that includes four virtual drones was developed for experimental purposes. A comparison was made among various methods during the simulations, and the results concluded that the recurrent, single-agent model is the most effective method, being 33% more effective than its recurrent, multi-agent counterparts. The poor performance of the non-recurrent, single-agent baseline model also suggests that the recurrent elements in the network are essential to enable desirable multiple-drones flight.

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“…Another approach currently being pursued by the research community to make deep learning models scalable is model compression and development of scalable computing algorithm for mobile platforms. Mademlis et al 127 provided a description of the requirements and functionalities to support the use of UAVs in filming in dynamic and unstructured environment with of focus on 2D and 3D vision-based techniques. Motlagh et al 124 also presented UAV-based Internet of Things (IOT) platform using 2D face recognition for detecting faces in a crowd for surveillance and using mmWave for downloading and communicating with edge computing cluster for processing to achieve real-time performance.…”

Section: Applicationsmentioning

confidence: 99%

A review of algorithms and techniques for image-based recognition and inference in mobile robotic systems

Tawiah

2020

International Journal of Advanced Robotic Systems

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Autonomous vehicles include driverless, self-driving and robotic cars, and other platforms capable of sensing and interacting with its environment and navigating without human help. On the other hand, semiautonomous vehicles achieve partial realization of autonomy with human intervention, for example, in driver-assisted vehicles. Autonomous vehicles first interact with their surrounding using mounted sensors. Typically, visual sensors are used to acquire images, and computer vision techniques, signal processing, machine learning, and other techniques are applied to acquire, process, and extract information. The control subsystem interprets sensory information to identify appropriate navigation path to its destination and action plan to carry out tasks. Feedbacks are also elicited from the environment to improve upon its behavior. To increase sensing accuracy, autonomous vehicles are equipped with many sensors [light detection and ranging (LiDARs), infrared, sonar, inertial measurement units, etc.], as well as communication subsystem. Autonomous vehicles face several challenges such as unknown environments, blind spots (unseen views), non-line-of-sight scenarios, poor performance of sensors due to weather conditions, sensor errors, false alarms, limited energy, limited computational resources, algorithmic complexity, human–machine communications, size, and weight constraints. To tackle these problems, several algorithmic approaches have been implemented covering design of sensors, processing, control, and navigation. The review seeks to provide up-to-date information on the requirements, algorithms, and main challenges in the use of machine vision–based techniques for navigation and control in autonomous vehicles. An application using land-based vehicle as an Internet of Thing-enabled platform for pedestrian detection and tracking is also presented.

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Autonomous Unmanned Aerial Vehicles Filming In Dynamic Unstructured Outdoor Environments [Applications Corner]

Cited by 42 publications

References 10 publications

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

Aerial filming with synchronized drones using reinforcement learning

A review of algorithms and techniques for image-based recognition and inference in mobile robotic systems

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