<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Unmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as land use surveys and traffic surveillance, and weather research. Many network protocols are architected for the communication between multiple drones. The present study proposes drone-following models for managing drones in the transportation management system in smart cities. These models are based on the initial idea that drones flight towards a leading drone in the traffic flow. Such models are described by the relative distance and velocity functions. Two types of drone-following models are presented in the study. The first model is a safe distance model (SD model), in which a safe distance between a drone and its ahead is maintained. By applying the stochastic diffusion process, an improved model, called Markov model, is deduced. These drone-following models are simulated in a 2D environment using numerical simulation techniques. With the simulation results, it could be noted that: i) there is no accident and no unrealistic deceleration; ii) the velocity of the followed drone is changed according to the speed of the drone ahead; iii) the followed drones keep a safe distance to drone ahead even the velocities are changed; iv) the performance of the Markov model is better than that of the SD model.</p></abstract>

Unmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as land use surveys and traffic surveillance, and weather research. Many network protocols are architected for the communication between multiple drones. The present study proposes drone-following models for managing drones in the transportation management system in smart cities. These models are based on the initial idea that drones flight towards a leading drone in the traffic flow. Such models are described by the relative distance and velocity functions. Two types of drone-following models are presented in the study. The first model is a safe distance model (SD model), in which a safe distance between a drone and its ahead is maintained. By applying the stochastic diffusion process, an improved model, called Markov model, is deduced. These drone-following models are simulated in a 2D environment using numerical simulation techniques. With the simulation results, it could be noted that: i) there is no accident and no unrealistic deceleration; ii) the velocity of the followed drone is changed according to the speed of the drone ahead; iii) the followed drones keep a safe distance to drone ahead even the velocities are changed; iv) the performance of the Markov model is better than that of the SD model.

Developing Models for Managing Drones in the Transportation System in Smart Cities

Department of Aeronautics, Naval Architecture and Railway Vehicles

Budapest University of Technology and Economics

Electrical, Control and Communication Engineering

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Unmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as...

Developing Models for Managing Drones in the Transportation System in Smart Cities

Data publikacji: 20 mar 2020

Zakres stron: 71 - 78

DOI: https://doi.org/10.2478/ecce-2019-0010

Słowa kluczowe
Air transportation, Mathematical model, Path planning, Safety management, Vehicle routing

© 2019 Nguyen Dinh Dung, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Developing Models for Managing Drones in the Transportation System in Smart Cities

Data publikacji: 20 mar 2020

Zakres stron: 71 - 78

DOI: https://doi.org/10.2478/ecce-2019-0010

Słowa kluczoweAir transportation, Mathematical model, Path planning, Safety management, Vehicle routing

© 2019 Nguyen Dinh Dung, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Słowa kluczowe
Air transportation, Mathematical model, Path planning, Safety management, Vehicle routing