Relevant bibliographies by topics / Quadrotors swarm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Quadrotors swarm'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Quadrotors swarm"

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Nakano, Reiichiro Christian S., Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Edwin Sybingco, Elmer P. Dadios, and Argel A. Bandala. "Utilization of the Physicomimetics Framework for Achieving Local, Decentralized, and Emergent Behavior in a Swarm of Quadrotor Unmanned Aerial Vehicles (QUAV)." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (2017): 189–96. http://dx.doi.org/10.20965/jaciii.2017.p0189.

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This paper presents the implementation of the physicomimetics framework in governing the behavior of a swarm of quadrotors. Each quadrotor uses only local information about itself and the neighboring quadrotors to determine its own movement by applying the principles of physicomimetics. Through these localized and relatively simple interactions, the swarm of quadrotors was able to organize itself into various structures and exhibit different swarm behaviors such as aggregation, obstacle avoidance, lattice formation, and dispersion.
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Xie, Yichen, Yuzhu Li, and Wei Dong. "Behavior Prediction Based Trust Evaluation for Adaptive Consensus of Quadrotors." Drones 6, no. 12 (2022): 371. http://dx.doi.org/10.3390/drones6120371.

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Without proper treatment, a malfunctional quadrotor may bring severe consequences, e.g., becoming out of control, to the whole swarm. To tackle this problem, we develop a trust evaluations based consensus protocol. Specifically, each quadrotor in the swarm communicates with its connected neighbors, exchanging behavior predictions. By comparing the predicted and the actual behaviors of its neighbor regarding a pre-defined tolerance, each quadrotor assigns trust values to determine potentially legitimate or malfunctional companions. On this basis, an online adaptive controller adjusts each weigh
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Joelianto, Endra, Daniel Christian, and Agus Samsi. "Swarm control of an unmanned quadrotor model with LQR weighting matrix optimization using genetic algorithm." Journal of Mechatronics, Electrical Power, and Vehicular Technology 11, no. 1 (2020): 1. http://dx.doi.org/10.14203/j.mev.2020.v11.1-10.

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Unmanned aerial vehicle (UAV) quadrotors have developed rapidly and continue to advance together with the development of new supporting technologies. However, the use of one quadrotor has many obstacles and compromises the ability of a UAV to complete complex missions that require the cooperation of more than one quadrotor. In nature, one interesting phenomenon is the behaviour of several organisms to always move in flocks (swarm), which allows them to find food more quickly and sustain life compared with when they move independently. In this paper, the swarm behaviour is applied to drive a sy
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Khodayari, Houri, Farshad Pazooki, and AliReza Khodayari. "Motion optimization algorithm designing for swarm quadrotors in application of grasping objects." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 11 (2018): 3938–51. http://dx.doi.org/10.1177/0954410018812615.

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In this study, the process of designing a motion optimization algorithm for swarm quadrotor robots is presented. Motions equations of swarm are written based on Lagrangian energy equations. A potential function is applied on the equations to optimize the swarm motion. The applied potential function enables each of the swarm members to move toward an independent target coordinate as motion starts and simultaneously connecting with other members. As a result, the necessity of having the members aggregated within an area close to the swarm center is eliminated. This algorithm is supposed to act o
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Apriaskar, Esa. "PURWARUPA SISTEM PENDETEKSI JARAK ANTAR QUADROTOR DENGAN SENSOR GPS." INOVTEK POLBENG 8, no. 2 (2018): 250. http://dx.doi.org/10.35314/ip.v8i2.768.

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Technology of UAV (Unmanned Aerial Vehicle) which is quite rapidly developing in recent years, is quadrotor. The increasing number of quadrotor utilization in various aspects of life is one of the factors driving the development of research on quadrotor technology. The ability of a quadrotor to determine its distance from other quadrotor is one of the important factors that can support the success of formation swarm of quadrotor. This research aimed to create a prototype of distance detection system capable of supporting the mission of the formation swarm of quadrotor. Two pairs of latitude an
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Faelden, Gerard Ely U., Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Edwin Sybingco, Elmer P. Dadios, and Argel A. Bandala. "Implementation of Swarm Social Foraging Behavior in Unmanned Aerial Vehicle (UAV) Quadrotor Swarm." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (2017): 197–204. http://dx.doi.org/10.20965/jaciii.2017.p0197.

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One of the novel approaches in multiple quadrotor control is swarm robotics. It aims to mimic social behaviors of animals and insects. This paper presents the physical implementation of the swarm social foraging behavior in unmanned aerial vehicle quadrotors. To achieve this, it first explores the basic behavior of aggregation. It is implemented over a quadrotor swarm test-bed that makes use of external motion capture cameras. The completed algorithm makes use of the artificial potential function model combined with the environment resource profile model. Results show successful demonstration
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Cardona, Gustavo A., Juan Ramirez-Rugeles, Eduardo Mojica-Nava, and Juan M. Calderon. "Visual victim detection and quadrotor-swarm coordination control in search and rescue environment." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2079. http://dx.doi.org/10.11591/ijece.v11i3.pp2079-2089.

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We propose a distributed victim-detection algorithm through visual information on quadrotors using convolutional neuronal networks (CNN) in a search and rescue environment. Describing the navigation algorithm, which allows quadrotors to avoid collisions. Secondly, when one quadrotor detects a possible victim, it causes its closest neighbors to disconnect from the main swarm and form a new sub-swarm around the victim, which validates the victim’s status. Thus, a formation control that permits to acquire information is performed based on the well-known rendezvous consensus algorithm. Finally, im
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Gustavo, A. Cardona, Ramirez-Rugeles Juan, Mojica-Nava Eduardo, and M. Calderon Juan. "Visual victim detection and quadrotor-swarm coordination control in search and rescue environment." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2079–89. https://doi.org/10.11591/ijece.v11i3.pp2079-2089.

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We propose a distributed victim-detection algorithm through visual information on quadrotors using convolutional neuronal networks (CNN) in a search and rescue environment. Describing the navigation algorithm, which allows quadrotors to avoid collisions. Secondly, when one quadrotor detects a possible victim, it causes its closest neighbors to disconnect from the main swarm and form a new sub-swarm around the victim, which validates the victim’s status. Thus, a formation control that permits to acquire information is performed based on the well-known rendezvous consensus algorithm. Final
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Carbone, Carlos, Oscar Garibaldi, and Zohre Kurt. "Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture." KnE Engineering 3, no. 1 (2018): 552. http://dx.doi.org/10.18502/keg.v3i1.1459.

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This paper summarizes the concept of swarm robotics and its applicability to crop inspections. To increase the agricultural yield it is essential to monitor the crop health. Hence, precision agriculture is becoming a common practice for farmers providing a system that can inspect the state of the plants (Khosla and others, 2010). One of the rising technologies used for agricultural inspections is the use of unmaned air vehicles (UAVs) which are used to take aerial pictures of the farms so that the images could be processed to extract data about the state of the crops (Das et al., 2015). For th
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Hovell, Kirk, Steve Ulrich, and Murat Bronz. "Learned Multiagent Real-Time Guidance with Applications to Quadrotor Runway Inspection." Field Robotics 2, no. 1 (2022): 1105–33. http://dx.doi.org/10.55417/fr.2022036.

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Aircraft runways are periodically inspected for debris and damage. Instead of having pilots coordinate the motion of the quadrotors manually or hand-crafting the desired quadrotor behavior into a guidance law, this paper reports the use of deep reinforcement learning to learn a closed-loop multiagent real-time guidance strategy for quadrotors to autonomously perform such inspections. This yields a significant reduction in engineering effort while enabling highly-flexible real-time performance. The runway is discretized into a number of rectangular tiles, which must all be visited for the runwa
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Dissertations / Theses on the topic "Quadrotors swarm"

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Shrit, Omar. "Automatic coordination of a quadcopters fleet using ad hoc communications." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG108.

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Dans cette thèse, nous étudions la conception d’un contrôleur décentralisé pour un ensemble de quadrotors. Les quadrotors sont organisés en leader et suiveurs. Le leader est piloté par l’homme, tandis que les suiveurs utilisent le contrôleur décentralisé pour suivre le leader. Les suiveurs sont autonomes et n’ont pas conscience du comportement du leader. La nouveauté de cette thèse est de s’appuyer sur des capteurs peu coûteux tels que des modules WiFi pour estimer les distances vers les quadrotors voisins. Afin de concevoir le contrôleur décentralisé, l’apprentissage itératif est utilisé et c
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Yi-LunHuang and 黃怡綸. "Dynamic Analysis and Control of Quadrotor Swarm under Behavior-Based Formation Flight." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/ecmubv.

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碩士<br>國立成功大學<br>航空太空工程學系<br>104<br>This paper provides a method of ‘behavior-based’ to do the path planning for ‘quadrotors’. As walking to the destination, if there are obstacles on the road, it is required to avoid them and keep the safety distance from other people. Simultaneously, move towards the destination. In other words, the overall behavior of walking is composed of different motor schemas, which serve as the basic unit of behavior specification for the navigation of a mobile robot. (e.g. move to goal, obstacle avoidance, collision avoidance and etc. ) With their own weighting gain t
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Book chapters on the topic "Quadrotors swarm"

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Lazim, Izzuddin M., Abdul Rashid Husain, Nurul Adilla Mohd Subha, Zaharuddin Mohamed, and Mohd Ariffanan Mohd Basri. "Optimal Formation Control of Multiple Quadrotors Based on Particle Swarm Optimization." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6463-0_11.

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Wang, Mingming, Jinjin Guo, Juntong Qi, Chong Wu, and Qun Chen. "Collision-Free Formation Control for Multiple Quadrotors Subject to Switching Topologies." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_63.

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Shrit, Omar, and Michèle Sebag. "I2SL: Learn How to Swarm Autonomous Quadrotors Using Iterative Imitation Supervised Learning." In Progress in Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86230-5_33.

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Aguilera-Ruiz, Mario, Luis Torres-Treviño, and Angel Rodríguez-Liñán. "Collective Motion of a Swarm of Simulated Quadrotors Using Repulsion, Attraction and Orientation Rules." In Advances in Computational Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62434-1_41.

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Wang, Jianan, Jingze Zhang, Chunyu Li, Weihao Song, Li Liang, and Chunyan Wang. "Differential Backstepping Control for Quadrotor Aircraft." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_30.

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Sanchez-Lopez, Jose Luis, Jesús Pestana, Paloma de la Puente, Adrian Carrio, and Pascual Campoy. "Visual Quadrotor Swarm for the IMAV 2013 Indoor Competition." In ROBOT2013: First Iberian Robotics Conference. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03653-3_5.

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Zheng, Zhiqiang, Haibin Duan, and Chen Wei. "Binary Pigeon-Inspired Optimization for Quadrotor Swarm Formation Control." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53956-6_7.

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Meng, Jianghao, Xiaoping Zhu, Jun Yang, and Yue Li. "Neural Network Based Adaptive Consensus of Multi-quadrotor System." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_157.

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Abdelhamid, Bounemeur, Chemachema Mohamed, Zahaf Abdelmalek, Bouzina Salah, and Bououden Sofiane. "Partical Swarm Optimization Based Adaptive Fault-Tolerant Control for Quadrotor Attitude Stabilization." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0045-5_45.

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Wang, Yi, Hui Ye, and Xiaofei Yang. "A Novel Cooperative Target-Enclosing Control for Multiple Quadrotor UAVs via Passivity-Based Approach." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_128.

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Conference papers on the topic "Quadrotors swarm"

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Vemprala, Sai, and Srikanth Saripalli. "Vision based Collaborative Localization for Swarms of Aerial Vehicles." In Vertical Flight Society 73rd Annual Forum & Technology Display. The Vertical Flight Society, 2017. http://dx.doi.org/10.4050/f-0073-2017-12204.

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We present a framework for localizing a swarm of multirotor micro aerial vehicles (MAV) through collaboration using vision based sensing. For MAVs equipped with monocular cameras, this technique, built upon a relative pose estimation strategy between two or more cameras, enables the MAVs to share information of a common map and thus estimate accurate metric poses between each other even through fast motion and changing environments. Synchronized feature detection, matching and robust tracking enable the use of multiple view geometry concepts for performing the estimation. Furthermore, we prese
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Zhang, Xuewei, Qingzhao Liu, Hongyu Cao, and Bailing Tian. "A Gradient-Free and Parallel Hierarchical Motion Planning Framework for Quadrotor Swarm." In 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2024. https://doi.org/10.1109/icarcv63323.2024.10821567.

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Yang, Guanghai, Shenquan Wang, Yulian Jiang, and Dongyan Xue. "Quadrotor Attitude Control Under Active Disturbance Rejection Control Method Based on Particle Swarm Optimization." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10695986.

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Huang, Zhehui, Zhaojing Yang, Rahul Krupani, Baskın Şenbaşlar, Sumeet Batra, and Gaurav S. Sukhatme. "Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611499.

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Hafez, Ishaq, and Rached Dhaouadi. "Hybrid Particle Swarm Optimization with Quasi-Newton Local Search for Quadrotor Altitude and Attitude Control." In 2024 8th International Conference on Robotics and Automation Sciences (ICRAS). IEEE, 2024. http://dx.doi.org/10.1109/icras62427.2024.10654483.

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Wang, Honglei, Min Li, Huimin Sui, Baolong Zhu, and Hui Lv. "Tracking Control of Quadrotor Using PID Controller Based on an Improved Beetle Swarm Optimization Algorithm." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065399.

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Xu, Zhaosheng, Le Wang, Jianxiang Xi, Junlong Li, and Nanchi Liu. "Directional Expulsion for Formation of Distributed Quadrotor UAV Swarm with Switching Directed Topologies through Induced Attack." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661774.

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Kushleyev, Aleksandr, Vijay Kumar, and Daniel Mellinger. "Towards A Swarm of Agile Micro Quadrotors." In Robotics: Science and Systems 2012. Robotics: Science and Systems Foundation, 2012. http://dx.doi.org/10.15607/rss.2012.viii.028.

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Du, Xintong, Carlos E. Luis, Marijan Vukosavljev, and Angela P. Schoellig. "Fast and In Sync: Periodic Swarm Patterns for Quadrotors." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794017.

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Shijith, N., and Meher Madhu Dharmana. "Sonar based terrain estimation & automatic landing of swarm quadrotors." In 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT). IEEE, 2017. http://dx.doi.org/10.1109/iccpct.2017.8074216.

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