Using swarm intelligence in unmanned aerial vehicles for unknown location fixed target search
Authors: 
Patricia de Sousa Paula, Wellington W. Ferreira Sarmento, Gabriel A. Louis Paillard, Miguel Franklin de CastroAuthors Info & Claims
Patricia de Sousa Paula, Wellington W. Ferreira Sarmento, Gabriel A. Louis Paillard, Miguel Franklin de CastroAuthors Info & ClaimsArticle No.: 20, Pages 1 - 8
Abstract
The context of this research is the use of bioinspired algorithms applied to unmanned aerial vehicles (UAV) to search for a fixed target of unknown location. A target can be a lost human being or a broken vehicle, for example. Swarm algorithms used with UAVs can be adapted to perform better than a simple scanning algorithm such as Parallel Path Finder. The Particle Swarm Optimization and Bat Algorithm algorithms are compared using constraints such as UAV battery life and the size of the search area. Thus, the best solution to this problem is shown, among the adapted ones, considering the applied restrictions.
References
[1]
S. Bitam, M. Batouche, and Talbi. 2010. A survey on bee colony algorithms. In IEEE International Symposium on Parallel Distributed Processing, Workshops and Phd Forum (IPDPSW).
[2]
L. Castro. 2010. Computação Natural - Uma Jornada Ilustrada. São Paulo: Livraria da Física.
[3]
H. Chen, X. Wang, and Y. Li. 2009. A Survey of Autonomous Control for UAV. In International Conference on Artificial Intelligence and Computational Intelligence, 2009.
[4]
S. Correa and R. Cerqueira. 2009. Computação autônoma: Conceitos, Infraestruturas e Soluções em Sistemas Distribuído. In 27o SBRC - Livro Texto dos Minicursos. 151--198.
[5]
Jesus Manuel de la Cruz, Eva Besada-Portas, Luis Torre-Cubillo, Bonifacio Andres-Toro, and Jose Antonio Lopez-Orozco. 2008. Evolutionary path planner for uavs in realistic environments. In 10th annual conference on Genetic and evolutionary computation, 2008. 1477--1484.
[6]
Patricia de Sousa Paula, Miguel Franklin de Castro, Gabriel A. Louis Paillard, and Wellington W. F. Sarmento. 2016. A Swarm Solution for a Cooperative and Self-organized Team of UAVs to Search Targets. In Proceedings of the 2016 8th Euro American Conference on Telematics and Information Systems (EATIS). IEEE Computer Society, Washington, DC, USA, 24--.
[7]
Z.H. Ding, M. Huang, and Z.R. Lu. 2016. Structural damage detection using artificial bee colony algorithm with hybrid search strategy. (2016).
[8]
M. Dorigo, V. Maniezzo, and Colorni. 1996. Ant System: Optimization by a Colony of Cooperating Agents. In IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART B CYBERNETICS. 29--41.
[9]
F. Dressler and O. Akan. 2009. A survey on bio-inspired networking. (2009).
[10]
Mauro S. Innocente and Paolo Grasso. 2019. Self-organising swarms of firefighting drones: Harnessing the powerof collective intelligence in decentralised multi-robot systems. Journal of Computational Science (2019).
[11]
J. Kennedy and R. Eberhart. 1995. Particle Swarm Optimization. In IEEE International Conference on Neural Networks. 1942--1948.
[12]
S. Li, X. Sun, and Y. Xu. 2006. Particle Swarm Optimization for Route Planning of Unmanned Aerial Vehicles. In IEEE International Conference on Information Acquisition, 2006.
[13]
R. McCune and G. Madey. 2013. Agent-Based Simulation of Cooperative Hunting with UAVs. In Symposium on Agent Directed Simulation, 2013.
[14]
Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Xin-She Yang. 2014. Binary Bat Algorithm. Neural Comput. Appl. 25, 3--4 (Sept. 2014), 663--681.
[15]
N. Nigam, S. Bieniawski, I. Kroo, and J. Vian. 2012. Control of Multiple UAVs for Persistent Surveillance: Algorithm and Flight Test Results. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 20 (2012), 1236--1251.
[16]
N. Palmieri, X. Yang, F. Rango, and S. Marano. 2019. Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption. Neural Computing and Applications (2019).
[17]
Duc Truong Pham, A. Ghanbarzadeh, Ebubekir Koç, Sameh Otri, Shafqat Rahim, and Monji Zaidi. 2006. The Bees Algoritm - A Novel Tool for Complex Optimisation. In 2nd International Virtual Conference on Intelligent Production Machines and Systems. 454--459.
[18]
L. Ribeiro. 2009. BiO4SeL: Uma Abordagem Baseada em Colônia de Formigas para a Otimizacão do Tempo de Vida de Redes de Sensores Sem Fio. 2009. Mestrado em Ciência da Computação. Centro de Tecnologia, Programa de Pós-Graduação em Ciência da Computação, Universidade Federal do Ceará, Fortaleza.
[19]
L. Ribeiro and M. Castro. 2010. BiO4SeL: A Bio-Inspired Routing Algorithm for Sensor Network Lifetime Optimization. In 17th International Conference on Telecommunications.
[20]
B. Sathyaraj, L. Jain, A. Finn, and S. Drake. 2008. Multiple UAVs path planning algorithms: a comparative study. Fuzzy Optimization and Decision Making 7 (2008), 257--267.
[21]
Patricia Suárez, Andrés Iglesias, and Akemi Gálvez. 2019. Make robots be bats: specializing robotic swarms to the Bat algorithm. (2019).
[22]
L. Wei and Z. Wei. 2009. Path Planning of UAVs Swarm Using Ant Colony System. In International Conference on Natural Computation, 2009.
[23]
Roberto Sadao Yokoyama, Bruno Yuji Lino Kimura, and Edson dos Santos Moreira. 2014. Secure Positioning in a UAV Swarm Using On-board Stereo Cameras. In Proceedings of the 29th Annual ACM Symposium on Applied Computing (SAC '14). ACM, New York, NY, USA, 769--774.
Index Terms
- Using swarm intelligence in unmanned aerial vehicles for unknown location fixed target search
Recommendations
Fuzzy Logic Based Approach to Design of Autonomous Landing System for Unmanned Aerial Vehicles
This paper is concerned with autonomous flight of UAVs and proposes a fuzzy logic based autonomous flight and landing system controller. Besides three fuzzy logic controllers which are developed for autonomous navigation for UAVs in a previous work as ...
Trajectory Planning for Emergency Landing of VTOL Fixed-Wing Unmanned Aerial Vehicles
In recent years, inspired by technological progress and the outstanding performance of Unmanned Aerial Vehicles (UAVs) in several local wars, the UAV industry has witnessed explosive development, widely used in communication relay, logistics, surveying ...
Optimal path planning for drones based on swarm intelligence algorithm
AbstractRecently, Drones and UAV research were becoming one of the interest topics for academia and industry, where it has been extensively addressed in the literature back the few years. Path planning of drones in an area with complex terrain or unknown ...
Comments
Information & Contributors
Information
Published In
November 2020
388 pages
ISBN:9781450377119
DOI:10.1145/3401895
- Conference Chair:
- Angélica Félix de Castro,
- General Chairs:
- Joaquim Sousa Pinto,
- Rogério Patrício Chagas do Nascimento,
- Program Chairs:
- Santiago Felici-Castell,
- José Javier Samper Zapater
Copyright © 2020 ACM.
© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 January 2021
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
EATIS 2020
EATIS 2020: 10th Euro American Conference on Telematics and Information Systems
November 25 - 27, 2020
Aveiro, Portugal
Acceptance Rates
Overall Acceptance Rate 17 of 64 submissions, 27%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 112Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in