Artículos de revistas: "Quadrotors swarm"

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Nakano, Reiichiro Christian S., Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Edwin Sybingco, Elmer P. Dadios y 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, n.º 2 (15 de marzo de 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 y Wei Dong. "Behavior Prediction Based Trust Evaluation for Adaptive Consensus of Quadrotors". Drones 6, n.º 12 (22 de noviembre de 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 y 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, n.º 1 (30 de julio de 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 y 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, n.º 11 (26 de noviembre de 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, n.º 2 (31 de diciembre de 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 y 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, n.º 2 (15 de marzo de 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 y 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, n.º 3 (1 de junio de 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 y Zohre Kurt. "Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture". KnE Engineering 3, n.º 1 (11 de febrero de 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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9

Hovell, Kirk, Steve Ulrich y Murat Bronz. "Learned Multiagent Real-Time Guidance with Applications to Quadrotor Runway Inspection". Field Robotics 2, n.º 1 (10 de marzo de 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 y Vijay Kumar. "Towards a swarm of agile micro quadrotors". Autonomous Robots 35, n.º 4 (10 de julio de 2013): 287–300. http://dx.doi.org/10.1007/s10514-013-9349-9.

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11

Bandala, Argel A., Elmer P. Dadios, Ryan Rhay P. Vicerra y Laurence A. Gan Lim. "Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors – Swarm Behavior for Aggregation, Foraging, Formation, and Tracking –". Journal of Advanced Computational Intelligence and Intelligent Informatics 18, n.º 5 (20 de septiembre de 2014): 745–51. http://dx.doi.org/10.20965/jaciii.2014.p0745.

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This paper presents the fusion of swarm behavior in multi robotic system specifically the quadrotors unmanned aerial vehicle (QUAV) operations. This study directed on using robot swarms because of its key feature of decentralized processing amongst its member. This characteristic leads to advantages of robot operations because an individual robot failure will not affect the group performance. The algorithm emulating the animal or insect swarm behaviors is presented in this paper and implemented into an artificial robotic agent (QUAV) in computer simulations. The simulation results concluded that for increasing number of QUAV the aggregation accuracy increases with an accuracy of 90.62%. The experiment for foraging revealed that the number of QUAV does not affect the accuracy of the swarm instead the iterations needed are greatly improved with an average of 160.53 iterations from 50 to 500 QUAV. For swarm tracking, the average accuracy is 89.23%. The accuracy of the swarm formation is 84.65%. These results clearly defined that the swarm system is accurate enough to perform the tasks and robust in any QUAV number.

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Weinstein, Aaron, Adam Cho, Giuseppe Loianno y Vijay Kumar. "Visual Inertial Odometry Swarm: An Autonomous Swarm of Vision-Based Quadrotors". IEEE Robotics and Automation Letters 3, n.º 3 (julio de 2018): 1801–7. http://dx.doi.org/10.1109/lra.2018.2800119.

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Joelianto, Endra, Winarendra Satya Rajasa y Agus Samsi. "Sistem Kontrol Swarm untuk Flocking Wahana NR-Awak Quadrotor dengan Optimasi Algoritma Genetik". Jurnal Teknologi Informasi dan Ilmu Komputer 8, n.º 6 (24 de noviembre de 2021): 1089. http://dx.doi.org/10.25126/jtiik.2021863467.

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Quadrotor merupakan wahana udara nir-awak jenis lepas landas atau pendaratan vertikal berbentuk silang dan memiliki sebuah rotor pada setiap ujung lengannya dengan kemampuan manuver yang tinggi. Swarm quadrotor yang terdiri dari sekumpulan quadrotor akan menjadi suatu swarm yang baik, sesuai dengan kriteria swarm oleh Reynold yaitu dapat menghindari tumbukan, menyamakan kecepatan, dan pemusatan swarm. Pengontrolan swarm quadrotor memiliki tingkat kerumitan yang tinggi karena melibatkan banyak agen. Riset pengembangan swarm quadrotor masih belum banyak dilakukan dan masih membuka peluang untuk meneliti dengan metoda lain yang lebih baik dalam menghasilkan swarm. Makalah ini mengusulkan pengontrolan swarm quadrotor yang terdiri dari dua tingkat lup kontrol. Lup pertama adalah pengontrol sistem model swarm untuk membangkitkan lintasan swarm dan lup kedua merupakan pengontrol pada quadrotor untuk melakukan penjejakan lintasan swarm. Pengontrol pertama menggunakan pengontrol proporsional derivatif (PD), sedangkan pengontrol kedua menggunakan regulator linier kuadratik (RLK). Pengontrol yang dirancang memiliki parameter yang banyak, sehingga pemilihan parameter yang optimal sangat sulit. Pencarian parameter optimal pada pengontrol model swarm quadrotor membutuhkan teknik optimasi seperti algoritma genetik (AG) untuk mengarahkan pencarian menuju solusi yang menghasilkan kinerja terbaik. Pada makalah ini, penalaan dengan optimasi AG hanya dilakukan pada pengontrol PD untuk menghasilkan lintasan swarm terbaik, sedangkan matrik bobot RLK dilakukan secara uji coba. Hasil simulasi swarm pada model quadrotor menunjukkan parameter , . , dan yang diperoleh menggunakan AG menghasilkan pergerakan swarm yang baik dengan kesalahan RMS pelacakan 0,0094 m terhadap fungsi obyektif. Sedangkan ketika parameter , dan dicari menggunakan AG, tidak berpengaruh banyak dalam memperbaiki hasil simulasi swarm quadrotor. AbstractThe quadrotor is a type of take-off or vertical landing unmanned aerial vehicles with a cross shape and has one rotor at each end of its arm with high maneuverability. A quadrotor swarm consisting of a group of quadrotors leads to a good swarm, according to Reynold's swarm criteria, which accomplishes collision avoidance, velocity matching, and flock centering. Quadrotor swarm control has a high level of complexity because it involves many agents. Research on the development of quadrotor swarm has received insignificant attention and it still opens opportunities to research other methods that are better at producing swarm. The paper proposes the control of a quadrotor swarm consisted of two levels of control loops. The first loop controls the swarm model system to generate the swarm trajectory and the second loop is the controller on the quadrotor to track the swarm path. The first controller uses a proportional derivative controller (PD), while the second controller uses the linear quadratic regulator (LQR). The controller that is designed has many parameters, so the optimal parameter selection is very difficult. The search for optimal parameters in the swarm model controller requires optimization techniques such as the genetic algorithm (GA) to direct the search for solutions that produce the best performance. In this paper, tuning with the optimization of GA is only done for the PD controller in order to produce the best swarm trajectory, while the weight matrices of the LQR are done on a trial error basis. Swarm simulation results of a quadrotor model system show the parameters , . , and obtained using GA produce a good swarm movement with RMS error 0.0094 m of the objective function. Whereas when parameters , and are searched using GA, it does not have much effect in improving the quadrotor swarm simulation results.

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Leonard, J., A. Savvaris y A. Tsourdos. "Distributed reactive collision avoidance for a swarm of quadrotors". Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, n.º 6 (18 de mayo de 2016): 1035–55. http://dx.doi.org/10.1177/0954410016647074.

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The large-scale of unmanned aerial vehicle applications has escalated significantly within the last few years, and the current research is slowly hinting at a move from single vehicle applications to multivehicle systems. As the number of agents operating in the same environment grows, conflict detection and resolution becomes one of the most important factors of the autonomous system to ensure the vehicles’ safety throughout the completion of their missions. The work presented in this paper describes the implementation of the novel distributed reactive collision avoidance algorithm proposed in the literature, improved to fit a swarm of quadrotor helicopters. The original method has been extended to function in dense and crowded environments with relevant spatial obstacle constraints and deconfliction manoeuvres for high number of vehicles. Additionally, the collision avoidance is modified to work in conjunction with a dynamic close formation flight scheme. The solution presented to the conflict detection and Resolution problem is reactive and distributed, making it well suited for real-time applications. The final avoidance algorithm is tested on a series of crowded scenarios to test its performances in close quarters.

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Yañez-Badillo, Hugo, Francisco Beltran-Carbajal, Ruben Tapia-Olvera, Antonio Favela-Contreras, Carlos Sotelo y David Sotelo. "Adaptive Robust Motion Control of Quadrotor Systems Using Artificial Neural Networks and Particle Swarm Optimization". Mathematics 9, n.º 19 (24 de septiembre de 2021): 2367. http://dx.doi.org/10.3390/math9192367.

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Most of the mechanical dynamic systems are subjected to parametric uncertainty, unmodeled dynamics, and undesired external vibrating disturbances while are motion controlled. In this regard, new adaptive and robust, advanced control theories have been developed to efficiently regulate the motion trajectories of these dynamic systems while dealing with several kinds of variable disturbances. In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introduced. Self-adaptive disturbance signal modeling based on Taylor-series expansions to handle dynamic uncertainty is adopted. Dynamic compensators of planned motion tracking errors are then used for designing a baseline controller with adaptive capabilities provided by three layers B-spline artificial neural networks (Bs-ANN). In the presented adaptive robust control scheme, measurements of position signals are only required. Moreover, real-time accurate estimation of time-varying disturbances and time derivatives of error signals are unnecessary. Integral reconstructors of velocity error signals are properly integrated in the output error signal feedback control scheme. In addition, the appropriate combination of several mathematical tools, such as particle swarm optimization (PSO), Bézier polynomials, artificial neural networks, and Taylor-series expansions, are advantageously exploited in the proposed control design perspective. In this fashion, the present contribution introduces a new adaptive desired motion tracking control solution based on B-spline neural networks, along with dynamic tracking error compensators for quadrotor non-linear systems. Several numeric experiments were performed to assess and highlight the effectiveness of the adaptive robust motion tracking control for a quadrotor unmanned aerial vehicle while subjected to undesired vibrating disturbances. Experiments include important scenarios that commonly face the quadrotors as path and trajectory tracking, take-off and landing, variations of the quadrotor nominal mass and basic navigation. Obtained results evidence a satisfactory quadrotor motion control while acceptable attenuation levels of vibrating disturbances are exhibited.

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Trizuljak, Adam, Frantiek Duchoň, Jozef Rodina, Andrej Babinec, Martin Dekan y Roman Mykhailyshyn. "Control of a small quadrotor for swarm operation". Journal of Electrical Engineering 70, n.º 1 (1 de febrero de 2019): 3–15. http://dx.doi.org/10.2478/jee-2019-0001.

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Abstract Small quadrotors, or so-called nanoquads, are widely available, typically have small take-off mass (between 12–50 g), and a flight time of about 5–10 minutes. The aim of this article is the proposal of control and development of the basic infrastructure for controlling a swarm nanoquads from an external computer and obtaining measurements from an onboard sensor. Control of nanoquad attitude and position is proposed and control allocation problem is addressed. Additionally, landing and collision detection is implemented using external disturbance force estimation. Results of the proposed control methods are verified in 4 scenarios: hover flight, manual control, step response, and collision and landing detection.

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McGuire, K. N., C. De Wagter, K. Tuyls, H. J. Kappen y G. C. H. E. de Croon. "Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment". Science Robotics 4, n.º 35 (23 de octubre de 2019): eaaw9710. http://dx.doi.org/10.1126/scirobotics.aaw9710.

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Swarms of tiny flying robots hold great potential for exploring unknown, indoor environments. Their small size allows them to move in narrow spaces, and their light weight makes them safe for operating around humans. Until now, this task has been out of reach due to the lack of adequate navigation strategies. The absence of external infrastructure implies that any positioning attempts must be performed by the robots themselves. State-of-the-art solutions, such as simultaneous localization and mapping, are still too resource demanding. This article presents the swarm gradient bug algorithm (SGBA), a minimal navigation solution that allows a swarm of tiny flying robots to autonomously explore an unknown environment and subsequently come back to the departure point. SGBA maximizes coverage by having robots travel in different directions away from the departure point. The robots navigate the environment and deal with static obstacles on the fly by means of visual odometry and wall-following behaviors. Moreover, they communicate with each other to avoid collisions and maximize search efficiency. To come back to the departure point, the robots perform a gradient search toward a home beacon. We studied the collective aspects of SGBA, demonstrating that it allows a group of 33-g commercial off-the-shelf quadrotors to successfully explore a real-world environment. The application potential is illustrated by a proof-of-concept search-and-rescue mission in which the robots captured images to find “victims” in an office environment. The developed algorithms generalize to other robot types and lay the basis for tackling other similarly complex missions with robot swarms in the future.

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Srisamosorn, Veerachart, Noriaki Kuwahara, Atsushi Yamashita, Taiki Ogata y Jun Ota. "Human-tracking system using quadrotors and multiple environmental cameras for face-tracking application". International Journal of Advanced Robotic Systems 14, n.º 5 (1 de septiembre de 2017): 172988141772735. http://dx.doi.org/10.1177/1729881417727357.

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In this article, a system for tracking human’s position and orientation in indoor environment was developed utilizing environmental cameras. The system consists of cameras installed in the environment at fixed locations and orientations, called environmental cameras, and a moving robot which mounts a camera, called moving camera. The environmental cameras detect the location and direction of each person in the space, as well as the position of the moving robot. The robot is then controlled to move and follow the person’s movement based on the person’s location and orientation, mimicking the act of moving camera tracking his/her face. The number of cameras needed to cover the area of the experiment, as well as each camera’s position and orientation, was obtained by using particle swarm optimization algorithm. Sensor fusion among multiple cameras is done by simple weighted averaging based on distance and knowledge of the number of robots being used. Xbox Kinect sensors and a miniature quadrotor were used to implement the system. The tracking experiment was done with one person walking and rotating in the area. The result shows that the proposed system can track the person and quadrotor within the degree of 10 cm , and the quadrotor can follow the person’s movement as desired. At least one camera was guaranteed to be tracking the person and the quadrotor at any time, with the minimum number of two for tracking the person and only a few moments that only one camera was tracking the quadrotor.

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Tsykunov, Evgeny, Ruslan Agishev, Roman Ibrahimov, Luiza Labazanova, Akerke Tleugazy y Dzmitry Tsetserukou. "SwarmTouch: Guiding a Swarm of Micro-Quadrotors With Impedance Control Using a Wearable Tactile Interface". IEEE Transactions on Haptics 12, n.º 3 (1 de julio de 2019): 363–74. http://dx.doi.org/10.1109/toh.2019.2927338.

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Elmokadem, Taha y Andrey V. Savkin. "Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking". Drones 5, n.º 4 (25 de octubre de 2021): 124. http://dx.doi.org/10.3390/drones5040124.

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This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robustness for the multi-vehicle system against vehicles’ failure. It is also computationally-efficient to ensure scalability, and it handles obstacle avoidance on a higher level to avoid conflicts in control with the inter-vehicle collision avoidance objective. The problem formulation is rather general considering mobile robots navigating in 3D spaces, which makes the proposed approach applicable to different UAV types and autonomous underwater vehicles (AUVs). However, implementation details have also been shown considering quadrotor-type UAVs for an example application in precision agriculture. Validation of the proposed methods have been performed using several simulations considering different simulation platforms such as MATLAB and Gazebo. Software-in-the-loop simulations were carried out to asses the real-time computational performance of the methods showing the actual implementation with quadrotors using C++ and the Robot Operating System (ROS) framework. Good results were obtained validating the performance of the suggested methods for coverage and flocking scenarios in 3D using systems with different sizes up to 100 vehicles. Some scenarios considering obstacle avoidance and robustness against vehicles’ failure were also used.

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Zhang, Yahui, Peng Yi y Yiguang Hong. "Cooperative Safe Trajectory Planning for Quadrotor Swarms". Sensors 24, n.º 2 (22 de enero de 2024): 707. http://dx.doi.org/10.3390/s24020707.

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In this paper, we propose a novel distributed algorithm based on model predictive control and alternating direction multiplier method (DMPC-ADMM) for cooperative trajectory planning of quadrotor swarms. First, a receding horizon trajectory planning optimization problem is constructed, in which the differential flatness property is used to deal with the nonlinear dynamics of quadrotors while we design a relaxed form of the discrete-time control barrier function (DCBF) constraint to balance feasibility and safety. Then, we decompose the original trajectory planning problem by ADMM and solve it in a fully distributed manner with peer-to-peer communication, which induces the quadrotors within the communication range to reach a consensus on their future trajectories to enhance safety. In addition, an event-triggered mechanism is designed to reduce the communication overhead. The simulation results verify that the trajectories generated by our method are real-time, safe, and smooth. A comprehensive comparison with the centralized strategy and several other distributed strategies in terms of real-time, safety, and feasibility verifies that our method is more suitable for the trajectory planning of large-scale quadrotor swarms.

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El gmili, Nada, Mostafa Mjahed, Abdeljalil El kari y Hassan Ayad. "Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach". Computational Intelligence and Neuroscience 2019 (24 de julio de 2019): 1–10. http://dx.doi.org/10.1155/2019/8925165.

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This paper explores the model parameters estimation of a quadrotor UAV by exploiting the cooperative particle swarm optimization-cuckoo search (PSO-CS). The PSO-CS regulates the convergence velocity benefiting from the capabilities of social thinking and local search in PSO and CS. To evaluate the efficiency of the proposed methods, it is regarded as important to apply these approaches for identifying the autonomous complex and nonlinear dynamics of the quadrotor. After defining the quadrotor dynamic modelling using Newton–Euler formalism, the quadrotor model’s parameters are extracted by using intelligent PSO, CS, PSO-CS, and the statistical least squares (LS) methods. Finally, simulation results prove that PSO and PSO-CS are more efficient in optimal tuning of parameters values for the quadrotor identification.

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Maningo, Jose Martin Z., Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Edwin Sybingco, Elmer P. Dadios y Argel A. Bandala. "Smoothed Particle Hydrodynamics Approach to Aggregation of Quadrotor Unmanned Aerial Vehicle Swarm". Journal of Advanced Computational Intelligence and Intelligent Informatics 21, n.º 2 (15 de marzo de 2017): 181–88. http://dx.doi.org/10.20965/jaciii.2017.p0181.

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This paper uses a fluid mechanics approach to perform swarming aggregation on a quadrotor unmanned aerial vehicle (QUAV) swarm platform. This is done by adapting the Smoothed Particle Hydrodynamics (SPH) technique. An algorithm benchmarking is conducted to see how well SPH performs. Simulations of varying set-ups are experimented to compare different algorithms with SPH. The position error of SPH is 30% less than the benchmark algorithm when a target enclosure is introduce. SPH is implemented using Crazyflie quadrotor swarm. The aggregation behavior exhibited successfully in the said platform.

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24

Zhou, De Xin, Xin Chao Ma y Teng Da Ma. "Path Planning of Quadrotor Based on Quantum Particle Swarm Optimization Algorithm". Advanced Materials Research 760-762 (septiembre de 2013): 2018–22. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2018.

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Nowadays, it becomes a hot research topic for autonomous flight of Quadrotor in the complex environment and the realization of fully autonomous flight is still a big challenge. The path planning of unmanned aerial vehicle is a key problem for its autonomous flight. For the path planning of Quadrotor, using the quantum particle swarm optimization algorithm, and made a lot of simulation and actual flight experiments. The results of simulation and actual flight experiment show that the using of QPSO for the path planning of Quadrotor is able to obtain a satisfactory result.

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25

Zhang, Qi, Yaoxing Wei y Xiao Li. "Quadrotor Attitude Control by Fractional-Order Fuzzy Particle Swarm Optimization-Based Active Disturbance Rejection Control". Applied Sciences 11, n.º 24 (7 de diciembre de 2021): 11583. http://dx.doi.org/10.3390/app112411583.

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In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of ADRC, a fractional-order fuzzy particle swarm optimization (FOFPSO) algorithm is proposed to optimize the parameters of ADRC for quadrotor aircraft. Simultaneously, the simulation experiment is designed, which compares with the optimized performance of traditional particle swarm optimization (PSO), fuzzy article swarm optimization (FPSO) and adaptive genetic algorithm-particle swarm optimization (AGA-PSO). In addition, the turbulent wind field model is established to verify the disturbance rejection performance of the controller. Finally, the designed controller is deployed to the actual hardware platform by using the model-based design method. The results show that the controller has a small overshoot and stronger disturbance rejection ability after the parameters are optimized by the proposed algorithm.

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26

Mohammed, Mohammed, Abduladhem Ali y Mofeed Rashid. "Fuzzy Petri Net Controller for Quadrotor System using Particle Swam Optimization". Iraqi Journal for Electrical and Electronic Engineering 11, n.º 1 (1 de junio de 2015): 132–44. http://dx.doi.org/10.37917/ijeee.11.1.14.

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In this paper, fuzzy Petri Net controller is used for Quadrotor system. The fuzzy Petrinet controller is arranged in the velocity PID form. The optimal values for the fuzzy Petri Net controller parameters have been achieved by using particle swarm optimization algorithm. In this paper, the reference trajectory is obtained from a reference model that can be designed to have the ideal required response of the Quadrotor, also using the quadrotor equations to find decoupling controller is first designed to reduce the effect of coupling between different inputs and outputs of quadrotor. The system performance has been measured by MATLAB. Simulation results showed that the FPN controller has a reasonable robustness against disturbances and good dynamic performance.

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27

Honig, Wolfgang, James A. Preiss, T. K. Satish Kumar, Gaurav S. Sukhatme y Nora Ayanian. "Trajectory Planning for Quadrotor Swarms". IEEE Transactions on Robotics 34, n.º 4 (agosto de 2018): 856–69. http://dx.doi.org/10.1109/tro.2018.2853613.

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28

Selma, Boumediene, Samira Chouraqui y Hassane Abouaïssa. "Fuzzy swarm trajectory tracking control of unmanned aerial vehicle". Journal of Computational Design and Engineering 7, n.º 4 (9 de abril de 2020): 435–47. http://dx.doi.org/10.1093/jcde/qwaa036.

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Abstract Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment of the ANFIS parameters to reduce tracking error and improve the quality of the controller. The results showed perfect behavior for the control law to control a UAV trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with ANFIS and PID control methods.

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29

El Gmili, Mjahed, El Kari y Ayad. "Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking". Applied Sciences 9, n.º 8 (25 de abril de 2019): 1719. http://dx.doi.org/10.3390/app9081719.

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This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) byexploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search(CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposedPSO-CS algorithm combines the ability of social thinking in PSO with the local search capability inCS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotordynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral,and Derivative) controllers are optimized by using the intelligent proposed approaches and theclassical method of Reference Model (RM) for quadrotor full control. Finally, simulation resultsprove that PSO and PSO-CS are more efficient in tuning of optimal parameters for the quadrotorcontrol. Indeed, the ability of PSO and PSO-CS to track the imposed trajectories is well seen from3D path tracking simulations and even in presence of wind disturbances.

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30

Xu, Linxing y Yang Li. "Distributed Robust Formation Tracking Control for Quadrotor UAVs with Unknown Parameters and Uncertain Disturbances". Aerospace 10, n.º 10 (28 de septiembre de 2023): 845. http://dx.doi.org/10.3390/aerospace10100845.

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In this paper, the distributed formation tracking control problem of quadrotor unmanned aerial vehicles is considered. Adaptive backstepping inherently accommodates model uncertainties and external disturbances, making it a robust choice for the dynamic and unpredictable environments in which unmanned aerial vehicles operate. This paper designs a formation flight control scheme for quadrotor unmanned aerial vehicles based on adaptive backstepping technology. The proposed control scheme is divided into two parts. For the position subsystem, a distributed robust formation tracking control scheme is developed to achieve formation flight of quadrotor unmanned aerial vehicles and track the desired flight trajectory. For the attitude subsystem, an adaptive disturbance rejection control scheme is proposed to achieve attitude stabilization during unmanned aerial vehicle flight under uncertain disturbances. Compared to existing results, the novelty of this paper lies in presenting a disturbance rejection flight control scheme for actual quadrotor unmanned aerial vehicle formations, without the need to know the model parameters of each unmanned aerial vehicle. Finally, a quadrotor unmanned aerial vehicle swarm system is used to verify the effectiveness of the proposed control scheme.

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31

Shen, Suiyuan y Jinfa Xu. "Attitude Active Disturbance Rejection Control of the Quadrotor and Its Parameter Tuning". International Journal of Aerospace Engineering 2020 (16 de noviembre de 2020): 1–15. http://dx.doi.org/10.1155/2020/8876177.

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The internal uncertainty and external disturbance of the quadrotor will have a significant impact on flight control. Therefore, to improve the control system’s dynamic performance and robustness, the attitude active disturbance rejection controller (ADRC) of the quadrotor is established. Simultaneously, an adaptive genetic algorithm-particle swarm optimization (AGA-PSO) is used to optimize the controller parameters to solve the problem that the controller parameters are difficult to tune. The performance of the proposed ADRC is compared with that of the sliding mode controller (SMC). The simulations revealed that the dynamic performance and robustness of the ADRC is better than that of the SMC.

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32

Can, Muharrem Selim y Hamdi Ercan. "Real-time tuning of PID controller based on optimization algorithms for a quadrotor". Aircraft Engineering and Aerospace Technology 94, n.º 3 (17 de noviembre de 2021): 418–30. http://dx.doi.org/10.1108/aeat-06-2021-0173.

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Purpose This study aims to develop a quadrotor with a robust control system against weight variations. A Proportional-Integral-Derivative (PID) controller based on Particle Swarm Optimization and Differential Evaluation to tune the parameters of PID has been implemented with real-time simulations of the quadrotor. Design/methodology/approach The optimization algorithms are combined with the PID control mechanism of the quadrotor to increase the performance of the trajectory tracking for a quadrotor. The dynamical model of the quadrotor is derived by using Newton-Euler equations. Findings In this study, the most efficient control parameters of the quadrotor are selected using evolutionary optimization algorithms in real-time simulations. The control parameters of PID directly affect the controller’s performance that position error and stability improved by tuning the parameters. Therefore, the optimization algorithms can be used to improve the trajectory tracking performance of the quadrotor. Practical implications The online optimization result showed that evolutionary algorithms improve the performance of the trajectory tracking of the quadrotor. Originality/value This study states the design of an optimized controller compared with manually tuned controller methods. Fitness functions are defined as a custom fitness function (overshoot, rise-time, settling-time and steady-state error), mean-square-error, root-mean-square-error and sum-square-error. In addition, all the simulations are performed based on a realistic simulation environment. Furthermore, the optimization process of the parameters is implemented in real-time that the proposed controller searches better parameters with real-time simulations and finds the optimal parameter online.

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33

Basri, Mohd Ariffanan Mohd. "Design and application of an adaptive backstepping sliding mode controller for a six-DOF quadrotor aerial robot". Robotica 36, n.º 11 (3 de agosto de 2018): 1701–27. http://dx.doi.org/10.1017/s0263574718000668.

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SUMMARYThe quadrotor aerial robot is a complex system and its dynamics involve nonlinearity, uncertainty, and coupling. In this paper, an adaptive backstepping sliding mode control (ABSMC) is presented for stabilizing, tracking, and position control of a quadrotor aerial robot subjected to external disturbances. The developed control structure integrates a backstepping and a sliding mode control approach. A sliding surface is introduced in a Lyapunov function of backstepping design in order to further improve robustness of the system. To attenuate a chattering problem, a saturation function is used to replace a discontinuous sign function. Moreover, to avoid a necessity for knowledge of a bound of external disturbance, an online adaptation law is derived. Particle swarm optimization (PSO) algorithm has been adopted to find parameters of the controller. Simulations using a dynamic model of a six degrees of freedom (DOF) quadrotor aerial robot show the effectiveness of the approach in performing stabilization and position control even in the presence of external disturbances.

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34

Wang, Yingxun, Yan Ma, Zhihao Cai y Jiang Zhao. "Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization- based backstepping control with sliding mode extended state observer". Transactions of the Institute of Measurement and Control 42, n.º 9 (7 de enero de 2020): 1675–89. http://dx.doi.org/10.1177/0142331219894401.

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In this paper, a new swarm intelligent-based backstepping control scheme is proposed for quadrotor trajectory tracking and obstacle avoidance. First, the sliding mode extended state observer (SMESO) is used to estimate different disturbances, and the tracking differentiator (TD) is integrated to enhance the performance of backstepping control scheme. Then, the chaotic grey wolf optimization (CGWO) is developed with chaotic initialization and chaotic search to optimize the parameters of attitude and position controllers. Further, the virtual target guidance approach is proposed for quadrotor trajectory tracking and obstacle avoidance. Comparative simulations and Monte Carlo tests are carried out to demonstrate the effectiveness and robustness of the CGWO-based backstepping control scheme and virtual target guidance approach.

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35

Oliveira, Josenalde, Paulo Moura Oliveira, José Boaventura-Cunha y Tatiana Pinho. "Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller". Robotics 9, n.º 2 (7 de abril de 2020): 22. http://dx.doi.org/10.3390/robotics9020022.

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The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

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36

Bandala, Argel A. y Elmer P. Dadios. "Dynamic Aggregation Method for Target Enclosure Using Smoothed Particle Hydrodynamics Technique – An Implementation in Quadrotor Unmanned Aerial Vehicles (QUAV) Swarm –". Journal of Advanced Computational Intelligence and Intelligent Informatics 20, n.º 1 (19 de enero de 2016): 84–91. http://dx.doi.org/10.20965/jaciii.2016.p0084.

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This paper presents an aggregation behavior derived from fluid characteristics by adapting Smoothed Particle Hydrodynamics (SPH) Technique. The most basic behavior in a swarm-like system is aggregation. The essential requirement of a swarm is to aggregate or collect itself in proximity to a singular point in order to execute higher level swarm behaviors. The aggregation behavior is further put into use by initiating a near convergence status in a single target enclosing it by the swarm with a given specific distance by using different fluid containers. In this paper, there are three fluid containers each is introduced with different characteristics. These containers are plane, spherical and toroidal containers. Using computer simulations with different trials, the proponents were able to determine the accuracy of containing the swarm elements in a desirable area. Furthermore, the ability of the swarm to maintain collectiveness is tested. The experiment results showed that the plane fluid container yielded an accuracy of 84.88%. A spherical fluid container displayed an accuracy of 95.23%. And using toroidal particle container showed an accuracy of 92.44%.

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37

Ayachi Chater, El, Halima Housny y Hassan El Fadil. "Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control". International Journal of Electrical and Computer Engineering (IJECE) 12, n.º 4 (1 de agosto de 2022): 3607. http://dx.doi.org/10.11591/ijece.v12i4.pp3607-3619.

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When the controlled system is subject to parameter variations and external disturbances, a fixed-parameter proportional integral derivative (PID) controller cannot ensure its stabilization. In this case, its control requires online parameter adjustment. Specifically, as the quadrotor is a multi-input multi-output, nonlinear, and underactuated system, robust control is necessary to ensure efficient trajectory tracking flights. In this paper, an adaptive proportional integral derivative (APID) controller is proposed to control the quadrotor systems. This APID-based control strategy uses a two hidden layer deep feedforward network (DFN), where the one-step secant algorithm is chosen for initializing the DFN parameters. All the design steps of the proposed adaptive controller are described. The multidimensional particle swarm optimization (PSO) algorithm is used for tuning the DFN parameters. Then, using two simulation efficiency tests, a comparison between the proposed PSO-based APID-DFN, the (non-optimized) APID-DFN, the feedforward network APID, and the fixed-parameter PID controllers proves much efficiency of the proposed adaptive controller. The results illustrate that the proposed PSO-based APID-DFN controller can ensure good quadrotor system stabilization and achieve minimum overshoot and faster convergence speed for all quadrotor motions. Thus, the proposed control strategy could be considered an additional intelligent method-based adaptive control for unmanned aerial vehicles.

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38

Qin, Zhenhao. "PID Control Algorithm Based on Particle Swarm Optimization for Quadrotor UAV with Tip Defect". Academic Journal of Science and Technology 7, n.º 2 (27 de septiembre de 2023): 101–5. http://dx.doi.org/10.54097/ajst.v7i2.11951.

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For the four-rotor UAV (Unmanned Aerial Vehicle), blade is one of the most important actuator, the four-rotor UAV is prone to blade tip defect during use, which will directly affect the reasoning size of the four-rotor UAV, resulting in the flight quality or performance decline of the four-rotor UAV, ordinary PID control in the case of blade tip defect, it can still be optimized by other algorithms. In this paper, particle swarm optimization will be used to optimize PID parameters in the case of tip defect of quadrotor UAV, and simulation experiments will be conducted in MATLAB Simulink to verify the reliability of particle swarm optimization by comparing and optimizing data curves such as forward and backward roll Angle and yaw Angle.

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39

Abdul-Samed, Baqir y Ammar Aldair. "Design Tunable Robust Controllers for Unmanned Aerial Vehicle Based on Particle Swarm Optimization Algorithm". Iraqi Journal for Electrical and Electronic Engineering 15, n.º 2 (1 de diciembre de 2019): 89–100. http://dx.doi.org/10.37917/ijeee.15.2.10.

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PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ) and for the motors of the quadrotor to make it more stable and efficient for doing its mission. The particle swarm optimization (PSO) algorithm is proposed in this work. The PSO algorithm is applied to tune the parameters of proposed PID controllers for the three layers to optimize the performances of the controlled system with and without existences of disturbance to show how the designed controller will be robust. The proposed controllers are used to control UAV, and the MATLAB 2018b is used to simulate the controlled system. The simulation results show that, the proposed controllers structure for the quadrotor improve the performance of the UAV and enhance its stability.

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40

Abdelghany, Muhammad Bakr, Ahmed M. Moustafa y Mohammed Moness. "Benchmarking Tracking Autopilots for Quadrotor Aerial Robotic System Using Heuristic Nonlinear Controllers". Drones 6, n.º 12 (26 de noviembre de 2022): 379. http://dx.doi.org/10.3390/drones6120379.

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This paper investigates and benchmarks quadrotor navigation and hold autopilots’ global control performance using heuristic optimization algorithms. The compared methods offer advantages in terms of computational effectiveness and efficiency to tune the optimum controller gains for highly nonlinear systems. A nonlinear dynamical model of the quadrotor using the Newton–Euler equations is modeled and validated. Using a modified particle swarm optimization (MPSO) and genetic algorithm (GA) from the heuristic paradigm, an offline optimization problem is formulated and solved for three different controllers: a proportional–derivative (PD) controller, a nonlinear sliding-mode controller (SMC), and a nonlinear backstepping controller (BSC). It is evident through the simulation case studies that the utilization of heuristic optimization techniques for nonlinear controllers considerably enhances the quadrotor system response. The performance of the conventional PD controller, SMC, and BSC is compared with heuristic approaches in terms of stability and influence of internal and external disturbance, and system response using the MATLAB/SIMULINK environment. The simulation results confirm the reliability of the proposed tuned GA and MPSO controllers. The PD controller gives the best performance when the quadrotor system operates at the equilibrium point, while SMC and BSC approaches give the best performance when the system does an aggressive maneuver outside the hovering condition. The overall final results show that the GA-tuned controllers can serve as a benchmark for comparing the global performance of aerial robotic control loops.

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41

Hong, Youkyung, Suseong Kim y Jihun Cha. "Integrated Global and Local Path Planning for Quadrotor Using Particle Swarm Optimization". IFAC-PapersOnLine 53, n.º 2 (2020): 15621–25. http://dx.doi.org/10.1016/j.ifacol.2020.12.2497.

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42

Bouallègue, Soufiene y Rabii Fessi. "LQG controller design for a quadrotor UAV based on particle swarm optimisation". International Journal of Automation and Control 13, n.º 5 (2019): 569. http://dx.doi.org/10.1504/ijaac.2019.10021363.

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43

Fessi, Rabii y Soufiene Bouallègue. "LQG controller design for a quadrotor UAV based on particle swarm optimisation". International Journal of Automation and Control 13, n.º 5 (2019): 569. http://dx.doi.org/10.1504/ijaac.2019.101910.

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44

Khodja, Mohammed Abdallah, Mohamed Tadjine, Mohamed Seghir Boucherit y Moussa Benzaoui. "Tuning PID attitude stabilization of a quadrotor using particle swarm optimization (experimental)". International Journal for Simulation and Multidisciplinary Design Optimization 8 (2017): A8. http://dx.doi.org/10.1051/smdo/2017001.

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45

Ju, Chanyoung y Hyoung Il Son. "A distributed swarm control for an agricultural multiple unmanned aerial vehicle system". Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 233, n.º 10 (21 de febrero de 2019): 1298–308. http://dx.doi.org/10.1177/0959651819828460.

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In this study, we propose a distributed swarm control algorithm for an agricultural multiple unmanned aerial vehicle system that enables a single operator to remotely control a multi-unmanned aerial vehicle system. The system has two control layers that consist of a teleoperation layer through which the operator inputs teleoperation commands via a haptic device and an unmanned aerial vehicle control layer through which the motion of unmanned aerial vehicles is controlled by a distributed swarm control algorithm. In the teleoperation layer, the operator controls the desired velocity of the unmanned aerial vehicle by manipulating the haptic device and simultaneously receives the haptic feedback. In the unmanned aerial vehicle control layer, the distributed swarm control consists of the following three control inputs: (1) velocity control of the unmanned aerial vehicle by a teleoperation command, (2) formation control to obtain the desired formation, and (3) collision avoidance control to avoid obstacles. The three controls are input to each unmanned aerial vehicle for the distributed system. The proposed algorithm is implemented in the dynamic simulator using robot operating system and Gazebo, and experimental results using four quadrotor-type unmanned aerial vehicles are presented to evaluate and verify the algorithm.

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46

Arul, Senthil Hariharan y Dinesh Manocha. "SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms Under Uncertainty". IEEE Robotics and Automation Letters 6, n.º 2 (abril de 2021): 2437–44. http://dx.doi.org/10.1109/lra.2021.3061975.

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47

Estevez, Julian, Jose M. Lopez-Guede y Manuel Graña. "Particle Swarm Optimization Quadrotor Control for Cooperative Aerial Transportation of Deformable Linear Objects". Cybernetics and Systems 47, n.º 1-2 (2 de enero de 2016): 4–16. http://dx.doi.org/10.1080/01969722.2016.1128759.

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48

Wang, Jia-Jun y Guang-Yu Liu. "Saturated control design of a quadrotor with heterogeneous comprehensive learning particle swarm optimization". Swarm and Evolutionary Computation 46 (mayo de 2019): 84–96. http://dx.doi.org/10.1016/j.swevo.2019.02.008.

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49

Rendón, Manuel A. y Felipe F. Martins. "Path Following Control Tuning for an Autonomous Unmanned Quadrotor Using Particle Swarm Optimization". IFAC-PapersOnLine 50, n.º 1 (julio de 2017): 325–30. http://dx.doi.org/10.1016/j.ifacol.2017.08.054.

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50

侯, 磊磊. "PID Parameter Tuning of Quadrotor Attitude Control Based on Mended Tunicate Swarm Algorithm". Journal of Sensor Technology and Application 12, n.º 02 (2024): 175–86. http://dx.doi.org/10.12677/jsta.2024.122020.

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