A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking

Pham, Huy Xuan; La, Hung Manh; Feil-Seifer, David; Deans, Matthew

doi:10.1109/tsmc.2018.2815988

Cited by 117 publications

(36 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the clustering scheme, multiple UAV groups are formed and deployed to the wild area [58]. Assume that a number This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: A System Modelmentioning

confidence: 99%

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Arafat

Moh

2021

IEEE Access

View full text Add to dashboard Cite

In dynamic unmanned aerial vehicle (UAV) networks, localization and clustering are fundamental functions for cooperative control. In this article, we propose bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring. First, we develop a hybrid gray wolf optimization (HGWO) method and propose an energy-efficient three-dimensional BIL algorithm based on the HGWO, which reduces localization errors, avoids flip ambiguity in bounded distance measurement errors, and achieves high localization accuracy. In BIL, the bounding cube method is applied to reduce the initial search space. Second, we propose an energy-efficient BIC algorithm based on the HGWO. The BIC algorithm utilizes the gray wolf leadership hierarchy to improve clustering efficiency. We also develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose a GWO-based compressive sensing (CS-GWO) algorithm to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to the BS, thereby reducing the routing delay and the number of transmissions. Our performance evaluation shows that the proposed BIL and BIC significantly outperform conventional schemes in terms of various performance metrics under different scenarios.INDEX TERMS Bio-inspired algorithm, cluster head, clustering, energy efficiency, gray wolf optimization, localization, network lifetime, routing protocol, unmanned aerial vehicle network.

show abstract

“…In the clustering scheme, multiple UAV groups are formed and deployed to the wild area [58]. Assume that a number This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: A System Modelmentioning

confidence: 99%

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Arafat

Moh

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…One of the most significant shortcomings of the centralized approaches is a lack of robustness (i.e., failure of the entire system if the central cleaning robot fails). Conversely, in the decentralized approaches [38], [39], each cleaning robot shares information directly with other cleaning robots without a central robot that assigns the tasks. Therefore, decentralized approaches are resistant to local changes or failures if some robots malfunction [40].…”

Section: Related Workmentioning

confidence: 99%

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Lee

Kang

2020

IEEE Access

View full text Add to dashboard Cite

Most cleaning robots have a good cleaning performance for small environments such as houses but require a longer cleaning time due to problems such as slow cleaning progress and low battery capacity, making the robots unsuitable for large environments such as libraries and airports. Cleaning large environments with multiple robots is faster than cleaning them with a single robot. Multi-cleaning robots can utilize several robots to simultaneously clean and share the task of cleaning among the robots. However, as the number of robots increases and the effective distribution of cleaning is not efficient during the cleaning process, the cleaning time will consequently be longer due to the frequent collisions between the robots. Therefore, to shorten the cleaning time, multi-cleaning robots require a coverage path planning that uses an effective cleaning distribution method in the cleaning process. In this paper, a coverage path planning using a cleaning distribution method based on map decomposition is proposed to reduce the cleaning time of multi-cleaning robots. The experimental results demonstrated that the proposed multi-cleaning robots' coverage path planning could be used in large environments in the presence of several types of obstacles. Furthermore, the cleaning time was found to be shorter than that of the previous methods in the case of multi-cleaning robots. INDEX TERMS Coverage path planning, cleaning robot, computational efficiency, multi-robots system, robot path planning.

show abstract

“…A comprehensive survey is given in [23]. Applications include underwater ship hull inspection [18], wildfire tracking with drones [40], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Complexity of planning for connected agents

Charrier

Queffelec

Sankur

et al. 2020

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

We study a variant of the Multi-Agent Path Finding (MAPF) problem in which the group of agents are required to stay connected with a supervising base station throughout the execution. In addition, we consider the problem of covering an area with the same connectivity constraint. We show that both problems are PSPACEcomplete on directed and undirected topological graphs while checking the existence of a bounded plan is NP-complete when the bound is given in unary (and PSPACEhard when the encoding is in binary). Moreover, we identify a realistic class of topological graphs on which the decision problem falls in NLOGSPACE although the bounded versions remain NP-complete for unary encoding.

show abstract

A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking

Cited by 117 publications

References 48 publications

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Complexity of planning for connected agents

Contact Info

Product

Resources

About