Pollution Source Localization Based on Multi-UAV Cooperative Communication

Fu, Zhangjie; Chen, Yiming; Ding, Yongjie; He, Daojing

doi:10.1109/access.2019.2900475

Cited by 59 publications

(29 citation statements)

References 29 publications

(33 reference statements)

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“…Finally, the MAV returned back to its starting position and landed. The whole experiment, along with a visualization of the sensor data, can be seen in a video on YouTube 13 . Figure 18 shows the MAV in front of the building along with a visualization of the sensor data and the detected window.…”

Section: Window Detection and The Outdoor-indoor Transitionmentioning

confidence: 99%

“…To reduce such hazards, a system described by [12] deploys an MAV with sensors capable of detection and measurement of the concentration of such gases. Multiple gas detecting MAVs deployed in parallel can be used for precise localization of the gas source [13]. Operations in places that are in the proximity of a source of radiation, such as the interior of a nuclear power plant, have to be planned with limited exposure time of human workers.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Firefighting Inside Buildings by an Unmanned Aerial Vehicle

et al. 2021

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This paper presents a novel approach to autonomous extinguishing of indoor fires inside a building by a Micro-scale Unmanned Aerial Vehicle (MAV). In particular, controlling and estimating the MAV state, detection of a building entrance, multi-modal MAV localization during the outdoorindoor transition, interior motion planning and exploration, fire detection and position estimation, and fire extinguishing are discussed. The performance of these elements, as well as of the entire integrated system, are evaluated in simulations and field tests in various demanding real-world conditions. The system presented here is part of a complex multi-MAV solution that won the Mohamed Bin Zayed International Robotics Challenge 2020 (MBZIRC 2020) competition, and is being used as the core of a fire-fighting Unmanned Aerial System (UAS) industrial platform under development. A video attachment to this paper is available at the website http://mrs.felk.cvut.cz/2020firechallenge-insidefires.

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Section: Window Detection and The Outdoor-indoor Transitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous Firefighting Inside Buildings by an Unmanned Aerial Vehicle

et al. 2021

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“…Besides, the location of pollution sources in chemical plants has become a hot research topic. An air pollution source tracking algorithm based on artificial potential field method and particle swarm optimization algorithm can accurately find the pollution source in a short time [137]. To monitor the degradation of tropical rain forests, [138] studied a new method for real-time automatic detection of non-forest and eroded areas in tropical rain forests.…”

Section: B) Environment Monitoring Fieldmentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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“…This behavior is resilient to external stimuli as long as the agents are within communication range. By using these methods, and despite the remaining challenges to designing the desired group behavior, the robotics community has used swarm-based approaches for applications such as target tracking [13][14][15][16][17], Search And Rescue (SAR) [18], or Odor Source Localization (OSL) [19][20][21][22][23][24][25][26], among others.…”

Section: Introductionmentioning

confidence: 99%

Control of a Robotic Swarm Formation to Track a Dynamic Target With Communication Constraints: Analysis and Simulation

Coquet¹,

Arnold²,

Bouvet³

2021

Preprint

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We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms and the PSO algorithm for function optimization. Four parameters drive LCPSO: the number of agents; the inertia weight; the attraction/repulsion weight; and the inter-agent distance. Using APF, we provide a mathematical analysis of the LCPSO algorithm under some simplifying assumptions. First, the swarm will aggregate and attain a stable formation, whatever the initial conditions. Second, the swarm moves thanks to an attractor in the swarm, which serves as a guide for the other agents to head for the target. By focusing on a simple application of target tracking with communication constraints, we then remove those assumptions one by one. We show the algorithm is resilient to constraints on the communication range, and the behavior of the target. Results on simulation confirm our theoretical analysis. This provides useful guidelines to understand and control the LCPSO algorithm as a function of swarm characteristics as well as the nature of the target.

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Pollution Source Localization Based on Multi-UAV Cooperative Communication

Cited by 59 publications

References 29 publications

Autonomous Firefighting Inside Buildings by an Unmanned Aerial Vehicle

Autonomous Firefighting Inside Buildings by an Unmanned Aerial Vehicle

UAV Swarm Intelligence: Recent Advances and Future Trends

Control of a Robotic Swarm Formation to Track a Dynamic Target With Communication Constraints: Analysis and Simulation

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