Relevant bibliographies by topics / Quadrotors swarm

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Academic literature on the topic 'Quadrotors swarm'

Author: Grafiati
Published: 25 May 2024

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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 weight in the protocol corresponding to the trust evaluations designed before. We prove that, within proper tolerance, it is almost sure that the legitimate quadrotors can identify the malfunctional quadrotors through trust evaluations and ameliorate their effects on the whole system. Almost surely, the legitimate quadrotors can converge to their center in a finite time. We verify our method through MATLAB and GAZEBO. In particular, with our proposed method, the swarm system discussed in this paper is able to reach position and velocity consensus in the presence of malfunctional quadrotors.
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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 system consisting of six UAV quadrotors as agents for flocking while tracking a swarm trajectory. The swarm control system is expected to minimize the objective function of the energy used and tracking errors. The considered swarm control system consists of two levels. The first higher level is a proportional – derivative type controller that produces the swarm trajectory to be followed by UAV quadrotor agents in swarming. In the second lower level, a linear quadratic regulator (LQR) is used by each UAV quadrotor agent to follow a tracking path well with the minimal objective function. A genetic algorithm is applied to find the optimal LQR weighting matrices as it is able to solve complex optimization problems. Simulation results indicate that the quadrotors' tracking performance improved by 36.00 %, whereas their swarming performance improved by 17.17 %.
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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 on swarm of quadrotors; therefore a validated dynamic model of quadrotor and a designed controller are introduced to discuss the possible applications. The designed algorithm is then applied to grasp an object. In order to establish grasping, particle swarm optimization method is used. Finally, the algorithm is simulated in MATLAB for a two-member swarm of quadrotors for grasping the object. Simulation results indicate increased work space for the members along the motion path and reduced mission time.
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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 and longitude angles data from GPS sensor which represented coordinate position of 2 quadrotors were calculated using haversine formula to get the distance between 2 quadrotors. Data resulted from the system are compared with actual distance to test the success of the system in calculating the distance between two quadrotor distance.
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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 of the social foraging algorithm with minimal error in position. Also, the proposed algorithm’s performance presents an increase in aggregation speed and time as the number of swarm member increases.
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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, images are processed using CNN identifying potential victims in the area. Given the uncertainty of the victim detection measurement among quadrotors’ cameras in the image processing, estimation consensus (EC) and max-estimation consensus (M-EC) algorithms are proposed focusing on agreeing over the victim detection estimation. We illustrate that M-EC delivers better results than EC in scenarios with poor visibility and uncertainty produced by fire and smoke. The algorithm proves that distributed fashion can obtain a more accurate result in decision-making on whether or not there is a victim, showing robustness under uncertainties and wrong measurements in comparison when a single quadrotor performs the mission. The well-functioning of the algorithm is evaluated by carrying out a simulation using V-Rep.
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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 this process both fixed wings and quadrotors UAVs are used with a preference over the quadrotor since it’s easier to operate and has a milder learning curve compared to fixed wings (Kolodny, 2017). UAVs require battery replacement especially when the environmental conditions result in longer inspection times (“Agriculture - Maximize Yields with Aerial Imaging,” n.d., “Matrice 100 - DJI Wiki,” n.d.). As a result, inspection systems for crops using commercial quadrotors are limited by the quadrotor´s maximum flight speed, maximum flight height, quadrotor´s battery time, crops area, wind conditions, etc. (“Mission Estimates,” n.d.).Keywords: Swarm Robotics, Precision Agriculture, Unmanned Air Vehicle, Quadrotor, inspection.
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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 runway to be considered inspected. The guidance system reported here calculates a desired acceleration in real time for the quadrotor(s) to track in order to complete the task. This paper first develops the guidance technique, trains it in simulation, and evaluates it experimentally using an indoor quadrotor laboratory. This process is then repeated for an outdoor setting on a real runway, where the proposed guidance strategy is compared to a handcrafted strategy and applied to a multiquadrotor scenario where the quadrotors must learn to coordinate their behavior and be resilient to the failure of one quadrotor mid-experiment. Multiagent, fault-tolerant, learned behavior is successfully demonstrated through outdoor quadrotor flights. Additional simulations and experiments demonstrate the technique is viable in a swarm with additional quadrotors, on a variety of runway shapes and with increased discretization of the runway. This work shows how modern learning-based techniques can: 1) reduce the engineering effort required to design complex guidance systems and 2) be implemented on real hardware in a representative outdoor environment.
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Kushleyev, Alex, Daniel Mellinger, Caitlin Powers, and Vijay Kumar. "Towards a swarm of agile micro quadrotors." Autonomous Robots 35, no. 4 (2013): 287–300. http://dx.doi.org/10.1007/s10514-013-9349-9.

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Dissertations / Theses on the topic "Quadrotors swarm"

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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 the motor schemas can produce a potential field which can generate the force to give the vehicle to decide where to go and how to go. In order to form the different formation, this paper use one of the formation position determination method- ‘leader-referenced’ to do the ‘formation control’ on the quadrotors.
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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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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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Baldini, Alessandro, Lucio Ciabattoni, Riccardo Felicetti, et al. "Particle Swarm Optimization Based Sliding Mode Control Design: Application to a Quadrotor Vehicle." In Studies in Computational Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55598-0_7.

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Conference papers on the topic "Quadrotors swarm"

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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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Leonard, Jeremie, Samer Aldhaher, Al Savvaris, and Antonios Tsourdos. "Automated Recharging Station for Swarm of Unmanned Aerial Vehicles." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-88246.

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Automated maintenance has become a necessity for UAV systems to allow human operators to concentrate on higher-level tasks. By reducing the need for a human interaction, such systems could be set to function in remote environments for an extended period of time and take care of a higher number of vehicles. This paper describes the work carried out to design, test and construct an autonomous charging station for battery-powered quadrotors. In the effort to fit swarm behaviors, the focus has been to shorten the charging phase of a single quadrotor platform. By designing safer electrical contacts and adding a cell balancer to the system, the station can supply considerably more current to charge the vehicle’s battery. Once the vehicle has landed, voltage and current probes transmit the current state-of-charge to a controller for optimum charging cycle. To support even more applications, the system was equipped with the capability of wireless power transfer. Energy is transferred from a power transmitter in the docking station to a power receiver on-board the vehicle based on resonant inductive coupling. Minimizing the internal losses of the DC/AC inverter and AC/DC rectifier in the transmitter and receiver will allow for higher power levels to be transmitted and will maximize the efficiency. With the continuous monitoring of the process and the advanced charging technologies allowing for a balanced high-current charge, the Flying/Charging ratio of the vehicle could reach 1.
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Mechali, Omar, Jamshed Iqbal, Jingxiang Wang, Xiaomei Xie, and Limei Xu. "Distributed Leader-Follower Formation Control of Quadrotors Swarm Subjected to Disturbances." In 2021 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2021. http://dx.doi.org/10.1109/icma52036.2021.9512623.

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Tsykunov, Evgeny, Ruslan Agishev, Roman Ibrahimov, et al. "SwarmCloak: Landing of a Swarm of Nano-Quadrotors on Human Arms." In SA '19: SIGGRAPH Asia 2019. ACM, 2019. http://dx.doi.org/10.1145/3355049.3360542.

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Bandala, Argel A., Ryan Rhay P. Vicerra, and Elmer P. Dadios. "Formation stabilization algorithm for swarm tracking in unmanned aerial vehicle (UAV) quadrotors." In TENCON 2014 - 2014 IEEE Region 10 Conference. IEEE, 2014. http://dx.doi.org/10.1109/tencon.2014.7022455.

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Tsykunov, E., L. Labazanova, A. Tleugazy, and D. Tsetserukou. "SwarmTouch: Tactile Interaction of Human with Impedance Controlled Swarm of Nano-Quadrotors." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8594424.

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Lu, Qi, Beibei Ren, and Yuan-dong Ji. "Uncertainty and Disturbance Estimator-Based Robust Region Tracking Control for Multiple Quadrotors." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3308.

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Abstract In this paper, the decentralized uncertainty and disturbance estimator (UDE)-based robust region reaching controller is developed to drive a swarm of quadrotors with the full degrees-of-freedom, nonlinear, coupled and underactuated dynamics to track the trajectory of a moving target region while avoiding collisions among themselves. The backstepping technique is utilized to seamlessly fuse the UDE into the region reaching control framework with the function of estimating and compensating the model uncertainties and external disturbances. Simulation studies are carried out to demonstrate the effectiveness of the proposed method for achieving the moving target trajectory tracking even in the presence of external disturbances.
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Furci, M., G. Casadei, R. Naldi, R. G. Sanfelice, and L. Marconi. "An open-source architecture for control and coordination of a swarm of micro-quadrotors." In 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2015. http://dx.doi.org/10.1109/icuas.2015.7152285.

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