Intelligent Unmanned Aerial Vehicle Platform for Smart Cities

Giyenko, Andrey; Cho, Young Im

doi:10.1109/scis-isis.2016.0159

Cited by 18 publications

(32 citation statements)

References 2 publications

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“…As not uncommon in the literature (Mhatre et al, 2015) (Choi et al, 2016) (Giyenko and Cho, 2016b), the onboard computing platform (running the control module (CM), the simulated mobile platform as well as the optional simulated sensor platform is a Raspberry Pi 2 (see Fig. 1) with the following specifications: a 900MHz quad-core ARM Cortex-A7 CPU, 1GB RAM, 4 USB ports, 40 GPIO pins, Full HDMI port, Ethernet port, combined 3.5mm audio jack and composite video, camera interface (CSI), display interface (DSI), Micro SD card slot, VideoCore IV 3D graphics core.…”

Section: Control Module (Cm)mentioning

confidence: 84%

“…The flight module (the hardware realizing all the flight operations, also used in, e.g., (Bupe et al, 2015) (Choi et al, 2016) (Giyenko and Cho, 2016b)) is a Pixhawk (see Fig. 1) featuring a 168Mhz 32-Bit STM32F427 Cortex M4, 256KB RAM, 2MB Flash, 14 PWM / servo outputs as well as connectivity options for additional peripherals (UART, I2C, CAN) and redundant power supply inputs as well as an automatic failover.…”

Section: Flight Module (Fm)mentioning

confidence: 99%

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Distributed Uav-Swarm-Based Real-Time Geomatic Data Collection Under Dynamically Changing Resolution Requirements

Almeida¹,

Hildmann

Solmaz³

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Unmanned Aerial Vehicles (UAVs) have been used for reconnaissance and surveillance missions as far back as the Vietnam War, but with the recent rapid increase in autonomy, precision and performance capabilities -and due to the massive reduction in cost and size -UAVs have become pervasive products, available and affordable for the general public. The use cases for UAVs are in the areas of disaster recovery, environmental mapping & protection and increasingly also as extended eyes and ears of civil security forces such as fire-fighters and emergency response units. In this paper we present a swarm algorithm that enables a fleet of autonomous UAVs to collectively perform sensing tasks related to environmental and rescue operations and to dynamically adapt to e.g. changing resolution requirements. We discuss the hardware used to build our own drones and the settings under which we validate the proposed approach.

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Section: Control Module (Cm)mentioning

confidence: 84%

Section: Flight Module (Fm)mentioning

confidence: 99%

Distributed Uav-Swarm-Based Real-Time Geomatic Data Collection Under Dynamically Changing Resolution Requirements

Almeida¹,

Hildmann

Solmaz³

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In the context of the article at hand we would like to emphasis 4 main application areas where UAVs are already used for various data-collection / sensing centered missions. These areas, which are briefly introduced below and for which the algorithm proposed in this article was mainly developed, are: Section 1.2.1: Precision agriculture [8,10,16,[22][23][24] Section 1.2.2: Disaster response and relief [9][10][11]13,16,21,[25][26][27] Section 1.2.3: Smart city applications and civil protection [9,11,16,21,28] Section 1.2.4: Land administration and wildlife protection [10,13,16,[29][30][31] 1.2.1. Precision agriculture…”

Section: Relevant Application Areasmentioning

confidence: 99%

“…Especially in the context of pest monitoring [22] and extreme conditions [16], it would be of added benefit to be able to deploy a swarm of devices that can cover an even larger area while at the same time being able to dispatch one of its members to investigate specific locations without the swarm losing sight of the entire field to do so. UAVs can be deployed [23] during or after disasters to contribute to disaster management operations by e.g., performing logistic tasks such the delivery of hardware and resources to incident sites [9], recovering hazardous material [16], assisting with traffic monitoring and management [10,16] or monitoring structures and inspecting terrain [13] before human operatives are permitted access. By augment or replacing remaining sensing capability [32] in an environment [9], UAVs can play a crucial role [23], especially when existing infrastructure is compromised, malfunctioning or disconnected.…”

Section: Relevant Application Areasmentioning

confidence: 99%

“…Recent developments have resulted in UAVs with acceptable performance becoming available at an affordable cost. The use of drones for preliminary data collection is becoming common practice for civilian applications in the areas of, for example, precision agriculture [8] and civil defense such as fire fighting [9], traffic management [10] or to locate victims [11].…”

Section: Introductionmentioning

confidence: 99%

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Nature-Inspired Drone Swarming for Real-Time Aerial Data-Collection Under Dynamic Operational Constraints

Hildmann

Kovács²,

Saffre

et al. 2019

Drones

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Unmanned Aerial Vehicles (UAVs) with acceptable performance are becoming commercially available at an affordable cost. Due to this, the use of drones for real-time data collection is becoming common practice by individual practitioners in the areas of e.g., precision agriculture and civil defense such as fire fighting. At the same time, as UAVs become a house-hold item, a plethora of issues—which can no longer be ignored and considered niche problems—are coming of age. These range from legal and ethical questions to technical matters such as how to implement and operate a communication infrastructure to maintain control over deployed devices. With these issues being addressed, approaches that focus on enabling collectives of devices to operate semi-autonomously are also increasing in relevance. In this article we present a nature-inspired algorithm that enables a UAV-swarm to operate as a collective which provides real-time data such as video footage. The collective is able to autonomously adapt to changing resolution requirements for specific locations within the area under surveillance. Our distributed approach significantly reduces the requirements on the communication infrastructure and mitigates the computational cost otherwise incurred. In addition, if the UAVs themselves were to be equipped with even rudimentary data-analysis capabilities, the swarm could react in real-time to the data it generates and self-regulate which locations within its operational area it focuses on. The approach was tested in a swarm of 25 UAVs; we present out preliminary performance evaluation.

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Smart Cities in the Era of Artificial Intelligence and Internet of Things: Promises and Challenges

Rjab

Mellouli

2021

Smart Cities and Smart Governance

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Intelligent Unmanned Aerial Vehicle Platform for Smart Cities

Cited by 18 publications

References 2 publications

Distributed Uav-Swarm-Based Real-Time Geomatic Data Collection Under Dynamically Changing Resolution Requirements

Distributed Uav-Swarm-Based Real-Time Geomatic Data Collection Under Dynamically Changing Resolution Requirements

Nature-Inspired Drone Swarming for Real-Time Aerial Data-Collection Under Dynamic Operational Constraints

Smart Cities in the Era of Artificial Intelligence and Internet of Things: Promises and Challenges

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