Drones to manage the urban environment: Risks, rewards, alternatives

Gallacher, David

doi:10.1139/juvs-2015-0040

Cited by 26 publications

(11 citation statements)

References 25 publications

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“…They proposed a cloud-based system for city-wide unmanned air traffic management and the analysis of control systems for collision avoidance [22]. A similar study was also carried out by Gallacher in 2015 for utilizing high-and low-altitude drones for urban environmental analysis and addressing the concerns about their security, privacy, and safety [23]. In 2016, Vattapparamban et al discussed the technical and societal concerns and challenges that need to be addressed in smart cities, such as privacy, cybersecurity, and public safety [24].…”

Section: Introductionmentioning

confidence: 97%

Minimum-Cost Drone–Nest Matching through the Kuhn–Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement

Mirzaeinia

Hassanalian

2019

Aerospace

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The development of new concepts for smart cities and the application of drones in this area requires different architecture for the drones’ stations (nests) and their placement. Drones’ stations are designed to protect drones from hazards and utilize charging mechanisms such as solar cells to recharge them. Increasing the number of drones in smart cities makes it harder to find the optimum station for each drone to go to after performing its mission. In classic ordered technique, each drone returns to its preassigned station, which is shown to be not very efficient. Greedy and Kuhn‒Munkres (Hungarian) algorithms are used to match the drone to the best nesting station. Three different scenarios are investigated in this study; (1) drones with the same level of energy, (2) drones with different levels of energy, and (3) drones and stations with different levels of energy. The results show that an energy consumption reduction of 25‒80% can be achieved by applying the Kuhn‒Munkres and greedy algorithms in drone‒nest matching compared to preassigned stations. A graphical user interface is also designed to demonstrate drone‒station matching through the Kuhn‒Munkres and greedy algorithms.

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Section: Introductionmentioning

confidence: 97%

Minimum-Cost Drone–Nest Matching through the Kuhn–Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement

Mirzaeinia

Hassanalian

2019

Aerospace

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“…Unmanned aerial vehicles (UAVs) or drones have recently become more popular in civil and commercial applications as they can accomplish many tasks which are beyond the human or ground vehicles' ability, by taking the advantages of drone's height climbing, far reaching and speed capabilities. The applications of UAVs can also enhance the efficiency of executing tasks and reduce the pressure of over- 1 College of Aerospace Engineering, Chongqing University, Chongqing 400044, China 2 Air Traffic Management Research Institute (ATMRI), Nanyang Technological University (NTU), Singapore 637460 3 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798; Principal Investigator of ATMRI's UAS Program 4 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798 crowded ground traffic [1]. It is more attractive if UAVs can be used in urban environments to facilitate people's lives, such as timely delivery, photography, or even air taxi in near future [2,3].…”

Section: Introductionmentioning

confidence: 99%

Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

Low

Pang

et al. 2021

IEEE Trans. Veh. Technol.

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Drones have a wide range of applications in urban environments as they can both enhance people's daily activities and commercial activities through various operations and deployments. With the increasing number of drones, flight safety and efficiency become the main concern, and effective drone operations can make a difference. Accordingly, 4D path planning for drone operations is the focus of this paper, and the swarm-based method is proposed to solve this complicated optimization problem. Under the framework of 'AirMatrix', the problem is solved in two levels, i.e., 3D path planning for a single drone and conflict resolution among drones. In the multipath planning level, multiple alternative flight paths for each drone are generated to increase the acceptance rate of a flight request. The constraints on a single flight path and two different flight paths are considered. The goal is to obtain several different short flight paths as alternatives. A clustering improved ant colony optimization (CIACO) algorithm is employed to solve the multi-path planning problem. The crowding mechanism is used in clustering, and some improvements are made to strengthen the global and local search ability in the early and later phases of iterations. In the task scheduling level, the conflicts between two drones are defined in two circumstances. One is for the time interval of passing the same path point, another one is for the right-angle collision between two drones. A three-layer fitness function is proposed to maximize the number of permitted flights according to the safety requirement, in which the airspace utilization and the operators' requests are both considered. A 'cross-off' strategy is developed to calculate the fitness value, and a 'distributed-centralized' strategy is applied considering the task priorities of drones. A genetic algorithm (GA)-based task scheduling algorithm is also developed according to the characteristic of the established model. Simulation results demonstrate that 4D flight path of each drone can be generated by the proposed swarmed-based algorithms, and safe and efficient drone operations in a specific airspace can be ensured.

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“…CO 2 , CH 4 , N 2 O, SO 2 , and toxic organic pollutants polychlorinated dibenzodioxins (PCDD), polychlorinated dibenzofurans (PCDF), polychlorinated biphenyls, particles, and nanoparticles of metals [17][18][19]. When measurements are carried out in hard-to-reach areas or near sources of pollution, the use of UAVs significantly extends the measurement possibilities, enabling a much better spatial and temporal resolution of measurement data compared to the results collected on the basis of tests from the ground or manned aircraft [20][21][22]. UAV can also be used for testing large areas, including hazardous areas, remote control allows for greater operational flexibility and measurement accuracy [23].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Selected CBRN Threats in the Air in Industrial Areas with the Use of Unmanned Aerial Vehicles

et al. 2020

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Unmanned aerial vehicles (UAVs) play an increasingly important role in various areas of life, including in terms of protection and security. As a result of fires, volcanic eruptions, or other emergencies, huge amounts of toxic gases, dust, and other substances are emitted into the environment, which, together with high temperature, often leads to serious environmental contamination. Based on the available literature and patent databases, an analysis of the available UAVs models was carried out in terms of their applicability in air contaminated conditions in industrial areas, in the event of emergencies, such as fire, chemical contamination. The possibilities of using the devices were analyzed in terms of weather conditions, construction, and used materials in CBRN (chemical, biological, radiological, nuclear) threat situations. It was found that, thanks to the use of appropriate sensors, cameras, and software of UAVs integrated with a given system, it is possible to obtain information on air quality at a given moment, which is very important for the safety of people and the environment. However, several elements, including the possibility of use in acidification conditions, requires refinement to changing crisis conditions.

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Drones to manage the urban environment: Risks, rewards, alternatives

Abstract: Drones to manage the urban environment. Risks, rewards, alternatives.

Cited by 26 publications

References 25 publications

Minimum-Cost Drone–Nest Matching through the Kuhn–Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement

Minimum-Cost Drone–Nest Matching through the Kuhn–Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement

Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

Monitoring of Selected CBRN Threats in the Air in Industrial Areas with the Use of Unmanned Aerial Vehicles

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