Log in

Relevant bibliographies by topics / Swarm guidance

Academic literature on the topic 'Swarm guidance'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Swarm guidance.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Swarm guidance"

1

Bassolillo, Salvatore Rosario, Luciano Blasi, Egidio D’Amato, Massimiliano Mattei, and Immacolata Notaro. "Decentralized Triangular Guidance Algorithms for Formations of UAVs." Drones 6, no. 1 (December 28, 2021): 7. http://dx.doi.org/10.3390/drones6010007.

Full text

Abstract:

This paper deals with the design of a guidance control system for a swarm of unmanned aerial systems flying at a given altitude, addressing flight formation requirements that can be formulated constraining the swarm to be on the nodes of a triangular mesh. Three decentralized guidance algorithms are presented. A classical fixed leader–follower scheme is compared with two alternative schemes: the former is based on the self-identification of one or more time-varying leaders; the latter is an algorithm without leaders. Several operational scenarios have been simulated involving swarms with obstacles and an increasing number of aircraft in order to prove the effectiveness of the proposed guidance schemes.

APA, Harvard, Vancouver, ISO, and other styles

2

Huang, Hanqiao, Xin Zhao, and Xiaoyan Zhang. "Intelligent Guidance and Control Methods for Missile Swarm." Computational Intelligence and Neuroscience 2022 (January 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/8235148.

Full text

Abstract:

High-speed unmanned aerial vehicles (UAVs) are more and more widely used in both military and civil fields at present, especially the missile swarm attack, and will play an irreplaceable key role in the future war as a special combat mode. This study summarizes the guidance and control methods of missile swarm attack operation. First, the traditional design ideas of the guidance and control system are introduced; then, the typical swarm attack guidance and control methods are analyzed by taking their respective characteristics into considering, and the limitations of the traditional design methods are given. On this basis, the study focuses on the advantages of intelligent integrated guidance and control design over traditional design ideas, summarizes the commonly used integrated guidance and control design methods and their applications, and explores the cooperative attack strategy of missile swarm suitable for the integrated guidance and control system. Finally, the challenges of missile swarm guidance and control are described, and the problems worthy of further research in the future are prospected. Summarizing the guidance and control methods of missile will contribute to the innovative research in this field, so as to promote the rapid development of unmanned swarm attack technology.

APA, Harvard, Vancouver, ISO, and other styles

3

Wu, Husheng, Qiang Peng, Meimei Shi, and Lining Xing. "A Survey of UAV Swarm Task Allocation Based on the Perspective of Coalition Formation." International Journal of Swarm Intelligence Research 13, no. 1 (January 1, 2022): 1–22. http://dx.doi.org/10.4018/ijsir.311499.

Full text

Abstract:

Coalition formation of unmanned aerial vehicle (UAV) swarms, an effective solution for UAV swarm task allocation, is an important technology for UAV swarms to perform real-time and efficient collaborative task allocation in a dynamic and unknown environment. This paper summarizes the task allocation methods of UAV swarm coalition comprehensively and systematically. First, starting with the related work of UAV swarm coalition task allocation, this paper introduces the basic concept, general model, and constraint index of UAV swarm coalition task allocation. Then, the specific content, research status, advantages, and disadvantages of the coalition formation methods are analyzed, respectively. Third, the commonly used solution algorithms and research status of coalition task allocation are introduced, and the advantages and disadvantages of the existing coalition formation solution algorithms are compared and analyzed. Finally, it provides significant guidance for future related research.

APA, Harvard, Vancouver, ISO, and other styles

4

Chen, Lin, Chi Wang, Chihang Yang, Hong Deng, and Hao Zhang. "Probabilistic Collision-free Pattern Control For Large-Scale Spacecraft Swarms Around Circular Orbits." Journal of Physics: Conference Series 2252, no. 1 (April 1, 2022): 012070. http://dx.doi.org/10.1088/1742-6596/2252/1/012070.

Full text

Abstract:

Abstract This work considers controlling large-scale spacecraft swarms to achieve complex spatial configuration. A novel distributed guidance algorithm is proposed based on Inhomogeneous Markov Chains, Probabilistic Density Guidance and Voronoi partition (IMC-PDG-Voronoi) algorithms. The physical space is partitioned into multiple bins and the density distribution of the swarm is controlled via a probabilistic approach. Then the modified Voronoi partition method is used to generate a collision-free trajectory for each agent. To apply the probabilistic control algorithm to circular Earth orbit, the periodic solution of the Clohessy-Wiltshire (C-W) equation in configuration space is transformed into a parameter space. Then a convex optimization open-loop controller with minimum fuel consumption in LVLH coordinates is designed to control the swarm to expected positions. Numerical simulations show that the algorithm can effectively guide and control large-scale spacecraft swarms to form complex configurations on circular orbits, with high precision and little cost.

APA, Harvard, Vancouver, ISO, and other styles

5

Chen, Lin, Chi Wang, Chihang Yang, Hong Deng, and Hao Zhang. "Probabilistic Collision-free Pattern Control For Large-Scale Spacecraft Swarms Around Circular Orbits." Journal of Physics: Conference Series 2252, no. 1 (April 1, 2022): 012070. http://dx.doi.org/10.1088/1742-6596/2252/1/012070.

Full text

Abstract:

Abstract This work considers controlling large-scale spacecraft swarms to achieve complex spatial configuration. A novel distributed guidance algorithm is proposed based on Inhomogeneous Markov Chains, Probabilistic Density Guidance and Voronoi partition (IMC-PDG-Voronoi) algorithms. The physical space is partitioned into multiple bins and the density distribution of the swarm is controlled via a probabilistic approach. Then the modified Voronoi partition method is used to generate a collision-free trajectory for each agent. To apply the probabilistic control algorithm to circular Earth orbit, the periodic solution of the Clohessy-Wiltshire (C-W) equation in configuration space is transformed into a parameter space. Then a convex optimization open-loop controller with minimum fuel consumption in LVLH coordinates is designed to control the swarm to expected positions. Numerical simulations show that the algorithm can effectively guide and control large-scale spacecraft swarms to form complex configurations on circular orbits, with high precision and little cost.

APA, Harvard, Vancouver, ISO, and other styles

6

Baxter, Daniel P., Adam J. Hepworth, Keith F. Joiner, and Hussein Abbass. "On the Premise of a Swarm Guidance Ontology for Human-Swarm Teaming." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 2249–53. http://dx.doi.org/10.1177/1071181322661541.

Full text

Abstract:

Effective Human-Swarm Teaming (HST) relies on bi-directional information flow between the human and the swarm. Systems with human control or oversight rely on information flow from the swarm to the humans to inform decisions, while information that flows back from humans is only that necessary for actuation, which remains primarily physical. To unlock the full potential of HSTs, the augmentation must extend into the overall logic of teaming, including both the human’s and machine’s cognitive domains, whereby an AI-equipped robot teammate is capable of complex cognitive functions. The effectiveness of HST will need a sufficient level of transparency in the interaction space formed by the bi-directional information flow between the human and the swarm. This transparency must continuously and constructively interpret the information exchanged between the human and the swarm to afford both cognitive agents with the capacity to form shared understanding and situation awareness, and thus, facilitating effective teaming through trust. An ontology is one formal representational construct that enables bi-directional interpretation, thus, transparency. In this paper, we conceptualise and present a meta-ontology for transparent HST interactions.

APA, Harvard, Vancouver, ISO, and other styles

7

Kung, Chien Chun, and Kuei Yi Chen. "Missile Guidance Algorithm Design Using Particle Swarm Optimization." Applied Mechanics and Materials 284-287 (January 2013): 2411–15. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2411.

Full text

Abstract:

This paper presents a technique to design a PSO guidance algorithm for the nonlinear and dynamic pursuit-evasion optimization problem. In the PSO guidance algorithm, the particle positions of the swarm are initialized randomly within the guidance command solution space. With the particle positions to be guidance commands, we predict and record missiles’ behavior by solving point-mass equations of motion during a defined short-range period. Taking relative distance to be the objective function, the fitness function is then evaluated according to the objective function. As the PSO algorithm proceeds, these guidance commands will migrate to a local optimum until the global optimum is reached. This paper implements the PSO guidance algorithm in two pursuit-evasion scenarios and the simulation results show that the proposed design technique is able to generate a missile guidance law which has satisfied performance in execution time, terminal miss distance, time of interception and robust pursuit capability.

APA, Harvard, Vancouver, ISO, and other styles

8

Puusepp, Andres, Tanel Tammet, and Enar Reilent. "Covering an Unknown Area with an RFID-Enabled Robot Swarm." Applied Mechanics and Materials 490-491 (January 2014): 1157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1157.

Full text

Abstract:

Our goal is to improve the coverage of an area using robots with simple sensors and simple, robust algorithms usable for any kind of room. We investigate the advantage of the swarm - compared to a single robot - and three different algorithms for the task of searching landmarks in a previously unknown area. The guidance of the robot is based on landmarks, implemented by RFID tags irregularly placed in the room. The experiments are conducted using a custom made simulator of RFID-equipped Roomba cleaning robots, based on our previous work with real-life Roomba swarms. We show that for the simple room coverage algorithms the speedup gained from increasing the size of the swarm diminishes as the swarm grows and most importantly, for larger swarm sizes the information available and the intelligence of the algorithm becomes less important.

APA, Harvard, Vancouver, ISO, and other styles

9

Kung, Chien-Chun, and Kuei-Yi Chen. "MISSILE GUIDANCE ALGORITHM DESIGN USING PARTICLE SWARM OPTIMIZATION." Transactions of the Canadian Society for Mechanical Engineering 37, no. 3 (September 2013): 971–79. http://dx.doi.org/10.1139/tcsme-2013-0083.

Full text

Abstract:

This paper presents a PSO guidance (PSOG) algorithm design for the pursuit-evasion optimization problem. The initialized particles are randomly set within the guidance command solution space and the relative distance is taken as the objective function. As the PSOG algorithm proceeds, the iteration will execute until the global optimum is reached. Two pursuit-evasion scenarios show that the PSOG algorithm has satisfied performance in execution time, terminal miss distance, time of interception, final stage turning rate and robust pursuit capability.

APA, Harvard, Vancouver, ISO, and other styles

10

Wu, Xiwei, Bing Xiao, Cihang Wu, and Yiming Guo. "Centroidal Voronoi Tessellation and Model Predictive Control–Based Macro-Micro Trajectory Optimization of Microsatellite Swarm." Space: Science & Technology 2022 (August 16, 2022): 1–10. http://dx.doi.org/10.34133/2022/9802195.

Full text

Abstract:

Probabilistic swarm guidance enables autonomous microsatellites to generate their individual trajectories independently so that the entire swarm converges to the desired distribution shape. However, it is essential to avoid crowding for reducing the possibility of collisions between microsatellites. To determine the collision-free guidance trajectory of each microsatellite from the current position to the target space, a collision avoidance algorithm is necessary. A synthesis method is proposed that generate the collision avoidance trajectories. The idea is that the trajectory planning is divided into macro-planning and micro-planning; macro-planning guides where the microsatellites move step by step from the initial cube to the target cube by probabilistic swarm guidance with Centroidal Voronoi tessellation, while the micro-planning is to generate the optimal path for each step and finally reach the specified position in the target cube by model predictive control. Simulation results are presented for the collision-free guidance trajectory of microsatellites to verify the benefits of this planning scheme.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Swarm guidance"

1

Schultz, Kevin M. "Distributed Agreement: Swarm Guidance to Cooperative Lighting." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1260968137.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Merrifield, Alistair James. "An Investigation Of Mathematical Models For Animal Group Movement, Using Classical And Statistical Approaches." Thesis, The University of Sydney, 2006. http://hdl.handle.net/2123/1132.

Full text

Abstract:

Collective actions of large animal groups result in elaborate behaviour, whose nature can be breathtaking in their complexity. Social organisation is the key to the origin of this behaviour and the mechanisms by which this organisation occurs are of particular interest. In this thesis, these mechanisms of social interactions and their consequences for group-level behaviour are explored. Social interactions amongst individuals are based on simple rules of attraction, alignment and orientation amongst neighbouring individuals. As part of this study, we will be interested in data that takes the form of a set of directions in space. In Chapter 2, we discuss relevant statistical measure and theory which will allow us to analyse directional data. These statistical tools will be employed on the results of the simulations of the mathematical models formulated in the course of the thesis. The first mathematical model for collective group behaviour is a Lagrangian self-organising model, which is formulated in Chapter 3. This model is based on basic social interactions between group members. Resulting collective behaviours and other related issues are examined during this chapter. Once we have an understanding of the model in Chapter 3, we use this model in Chapter 4 to investigate the idea of guidance of large groups by a select number of individuals. These individuals are privy to information regarding the location of a specific goal. This is used to explore a mechanism proposed for honeybee (Apis mellifera) swarm migrations. The spherical theory introduced in Chapter 2 will prove to be particularly useful in analysing the results of the modelling. In Chapter 5, we introduce a second mathematical model for aggregative behaviour. The model uses ideas from electromagnetic forces and particle physics, reinterpreting them in the context of social forces. While attraction and repulsion terms have been included in similar models in past literature, we introduce an orientation force to our model and show the requirement of a dissipative force to prevent individuals from escaping from the confines of the group.

APA, Harvard, Vancouver, ISO, and other styles

3

Merrifield, Alistair James. "An Investigation Of Mathematical Models For Animal Group Movement, Using Classical And Statistical Approaches." University of Sydney, 2006. http://hdl.handle.net/2123/1132.

Full text

Abstract:

Doctor of Philosophy
Collective actions of large animal groups result in elaborate behaviour, whose nature can be breathtaking in their complexity. Social organisation is the key to the origin of this behaviour and the mechanisms by which this organisation occurs are of particular interest. In this thesis, these mechanisms of social interactions and their consequences for group-level behaviour are explored. Social interactions amongst individuals are based on simple rules of attraction, alignment and orientation amongst neighbouring individuals. As part of this study, we will be interested in data that takes the form of a set of directions in space. In Chapter 2, we discuss relevant statistical measure and theory which will allow us to analyse directional data. These statistical tools will be employed on the results of the simulations of the mathematical models formulated in the course of the thesis. The first mathematical model for collective group behaviour is a Lagrangian self-organising model, which is formulated in Chapter 3. This model is based on basic social interactions between group members. Resulting collective behaviours and other related issues are examined during this chapter. Once we have an understanding of the model in Chapter 3, we use this model in Chapter 4 to investigate the idea of guidance of large groups by a select number of individuals. These individuals are privy to information regarding the location of a specific goal. This is used to explore a mechanism proposed for honeybee (Apis mellifera) swarm migrations. The spherical theory introduced in Chapter 2 will prove to be particularly useful in analysing the results of the modelling. In Chapter 5, we introduce a second mathematical model for aggregative behaviour. The model uses ideas from electromagnetic forces and particle physics, reinterpreting them in the context of social forces. While attraction and repulsion terms have been included in similar models in past literature, we introduce an orientation force to our model and show the requirement of a dissipative force to prevent individuals from escaping from the confines of the group.

APA, Harvard, Vancouver, ISO, and other styles

4

Novák, Jiří. "Návrh autopilota a letových řídících módů v prostředí Simulink." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-416616.

Full text

Abstract:

Tato diplomová práce je zaměřena na vývoj simulačního prostředí v Matlab/Simulink zvoleného letadla ve známém letovém režimu. Pozice a orientace letadla pohybujícího se ve vzduchu je popsána pohybovými rovnicemi se šesti stup\v{n}i volnosti. Soustava translačních, rotačních a kinematických rovnic tvoří soustavu devíti nelineárních diferenciálních rovnic prvního řádu. Tyto rovnice lze linearizovat okolo nějakého rovnovážného stavu, který budeme nazývat letovým režimem. Součástí simulačního prostředí je řídící systém letadla založený na PID regulaci. Základem je návrh autopilota, který řídí úhel podélného sklonu a úhel příčného náklonu. Součástí návrhu jsou takzvané „flight director\textquotedblright \phantom{s}m\'dy jako udržení výšky, volba kursu, regulace vertikální rychlosti, změna výšky, zachycení požadované výšky a navigační m\'{o}d založený na nelineárním navigačním zákonu. Optimalizace regulátorů za použití PSO algoritmu a Pareto optimalitě je využita pro nastavení parametrů PID regulátoru. Simulační prostředí je vizualizováno v softwaru FlightGear.

APA, Harvard, Vancouver, ISO, and other styles

5

Sunny, Ajin. "SINGLE-DEGREE-OF-FREEDOM EXPERIMENTS DEMONSTRATING ELECTROMAGNETIC FORMATION FLYING FOR SMALL SATELLITE SWARMS USING PIECEWISE-SINUSOIDAL CONTROLS." UKnowledge, 2019. https://uknowledge.uky.edu/me_etds/146.

Full text

Abstract:

This thesis presents a decentralized electromagnetic formation flying (EMFF) control method using frequency-multiplexed sinusoidal control signals. We demonstrate the EMFF control approach in open-loop and closed-loop control experiments using a single-degree-of-freedom testbed with an electromagnetic actuation system (EAS). The EAS sense the relative position and velocity between satellites and implement a frequency-multiplexed sinusoidal control signal. We use a laser-rangefinder device to capture the relative position and an ARM-based microcontroller to implement the closed-loop control algorithm. We custom-design and build the EAS that implements the formation control in one dimension. The experimental results in this thesis demonstrate the feasibility of the decentralized formation control algorithm between two satellites.

APA, Harvard, Vancouver, ISO, and other styles

6

Wu, Dai-Fen, and 吳岱芬. "Multi-Swarm Differential Evolution with Center of Solution Guidance." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/34817464039019777718.

Full text

Abstract:

碩士
國立東華大學
電機工程學系
103
This paper presents a novel multi-swarm differential evolution with center of solution guidance (MSDECOS). Center of solution improves convergence speed of differential evolution and enhances the effectiveness of algorithm. The concept of multi-swarm is that several swarms have their own strategy to search solution space and information to exchange with each other for getting better performance than single swarm. Wholebest is one of the exchanged information and makes individual move toward optimal solution. Although center of solution improves convergence speed, the feature may make individual fall into local optimal solution. In order to solve the problem, we used center of solution to judge movement of each swarm. The best solution of each swarm was regarded as a condition to decide whether to change structure of swarm. This mechanism can avoid each swarm falling into local optimal solution. We use benchmarks of CEC2013 to evaluate MSDECOS algorithm by average, standard deviation, convergence speed, analysis effective and robustness of MSDECOS. The proposed method gets effective search ability when testing and comparing these results with original DE for different strategies.

APA, Harvard, Vancouver, ISO, and other styles

7

Cheng-Ping, Chu, and 朱正平. "The Application of Particle Swarm Optimization to Missile Guidance." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/92283565724002863345.

Full text

Abstract:

碩士
國防大學理工學院
兵器系統工程碩士班
99
Particle swarm optimization is investigated in this thesis to perform missile guidance law design and simulations. The particle swarm optimization algorithm is introduced first. An example is presented to demonstrate the PSO and parameter effects. The equations of motion for the missile are then introduced. The PSO application process to missile guidance law design is then shown. The parameters, e.g., learning rate, population and initial guess scaling are studied as well. The simulation results show that the particle swarm optimization guidance law can guide the missile to intercept targets with many different trajectories. Comparisons between the PSO guidance law and pursuit guidance law are demonstrated showing that the PSO guidance can reduce the pursuit time and distance. Improved results using the Takuchi Design Method to set parameters are demonstrated as well.

APA, Harvard, Vancouver, ISO, and other styles

8

"Aspekte van beroepsoriëntering van swart stedelike leerlinge." Thesis, 2014. http://hdl.handle.net/10210/12955.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Swarm guidance"

1

LAND.TECHNIK 2022. VDI Verlag, 2022. http://dx.doi.org/10.51202/9783181023952.

Full text

Abstract:

INHALT Electrical Agricultural Machines Structuring of electrified agricultural machine systems – Diversity of solutions and analysis methods .....1 GridCON2 – Development of a Cable Drum Vehicle Concept to Power 1MW Fully Electric Agricultural Swarms ..... 11 GridCON Swarm – Development of a Grid Connected Fully Autonomous Agricultural Production System ..... 17 Fully electric Tractor with 1000 kWh battery capacity ..... 23 Soil and Modelling The Integration of a Scientific Soil Compaction Risk Indicator (TERRANIMO) into a Holistic Tractor and Implement Optimization System (CEMOS) .....29 Identification of draft force characteristics for a tillage tine with variable geometry ..... 37 Calibration of soil models within the Discrete Element Method (DEM) ..... 45 Automation and Optimization of Working Speed and Depth in Agricultural Soil Tillage with a Model Predictive Control based on Machine Learning ..... 55 Synchronising machine adjustments of combine harvesters for higher fleet performance ..... 65 A generic approach to bridge the gap between route optimization and motion planning for specific guidance points o...

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Swarm guidance"

1

Horayama, Keita, Daisuke Kurabayashi, Syarif Ahmad, Ayaka Hashimoto, Takuro Moriyama, and Tatsuki Choh. "Guidance of Robot Swarm by Phase Gradient in 3D Space." In Intelligent Robotics and Applications, 444–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43506-0_39.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Liao, Jingxian, and Hyochoong Bang. "Augmented Lagrange Based Particle Swarm Optimization for Missile Interception Guidance." In Lecture Notes in Electrical Engineering, 411–21. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2635-8_30.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Baxter, Daniel, Matthew Garratt, and Hussein A. Abbass. "Simulating Single and Multiple Sheepdogs Guidance of a Sheep Swarm." In Unmanned System Technologies, 51–65. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-60898-9_3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Li, Zhen-xing, Zhao-gang Wang, and Dong Li. "Guidance Instrumentation Systematic Error Separation Method Based on Particle Swarm Optimization." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 491–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32456-8_53.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Shi, Zhexin, Qing Wang, Jianglong Yu, Xiwang Dong, Ze Zhang, Qingdong Li, and Zhang Ren. "Cooperative Guidance Control with Collision Avoidance and Obstacle Dodging Mechanism." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control, 1236–52. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_117.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Zhou, Yuan, Yongfang Liu, Shuo Yang, Yuting Feng, and Yu Zhao. "Distributed Guidance Strategy with an Appointed Cooperative Time over Directed Network." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control, 155–66. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Liu, Tao, Xiao Wang, Fengqi Zheng, Tun Zhao, and Enmi Yong. "Cooperative Guidance Law of Multiple Interceptors Based on Neighborhood Optimal Control." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control, 1820–30. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_169.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Guo, Weian, Ming Chen, Lei Wang, and Qidi Wu. "Grouping Particle Swarm Optimizer with $$P_{best}$$ s Guidance for Large Scale Optimization." In Lecture Notes in Computer Science, 627–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41000-5_63.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Tang, Zhongnan, Yujie Wang, Qingyang Chen, Zhuo Liu, Xixiang Yang, and Zhongxi Hou. "A Guidance Law for Cooperative Attack of Multi-UAVs with Spatio-Temporal Constraints." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control, 1397–408. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Ma, Meng-chen, Shen-min Song, and Bin Liu. "Three-Dimensional Prescribed Performance Space-Time Cooperative Guidance Law for Intercepting Maneuvering Target." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control, 1225–35. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_116.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Swarm guidance"

1

Bandyopadhyay, Saptarshi, Soon-Jo Chung, and Fred Y. Hadaegh. "Probabilistic swarm guidance using optimal transport." In 2014 IEEE Conference on Control Applications (CCA). IEEE, 2014. http://dx.doi.org/10.1109/cca.2014.6981395.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Truman, Samuel, Jakob Seitz, and Sebastian von Mammen. "Stigmergic, Diegetic Guidance of Swarm Construction." In 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). IEEE, 2021. http://dx.doi.org/10.1109/acsos-c52956.2021.00062.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Lynn, Nandar, and P. N. Suganthan. "Comprehensive learning particle swarm optimizer with guidance vector selection." In 2013 IEEE Symposium on Swarm Intelligence (SIS). IEEE, 2013. http://dx.doi.org/10.1109/sis.2013.6615162.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Morgan, Daniel, Soon-Jo Chung, and Fred Y. Hadaegh. "Swarm Assignment and Trajectory Optimization Using Variable-Swarm, Distributed Auction Assignment and Model Predictive Control." In AIAA Guidance, Navigation, and Control Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-0599.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Acikmese, Behcet, and David S. Bayard. "Probabilistic swarm guidance for collaborative autonomous agents." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859358.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Pollini, Lorenzo, Marta Niccolini, Michele Rosellini, and Mario Innocenti. "Human-Swarm Interface for Abstraction Based Control." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-5652.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Acikmese, B., and D. S. Bayard. "A Markov chain approach to probabilistic swarm guidance." In 2012 American Control Conference - ACC 2012. IEEE, 2012. http://dx.doi.org/10.1109/acc.2012.6314729.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Beck, Charles, Jovany Avila, and Michael Frye. "Guidance and Navigation Controls for Drone Swarm Applications." In 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC). IEEE, 2022. http://dx.doi.org/10.1109/dasc55683.2022.9925745.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Liang, Xiaolong, Liu Liu, Jiaqiang Zhang, and Shenghou Li. "Aviation Swarm and Intelligent Air Combat." In 2018 IEEE CSAA Guidance, Navigation and Control Conference (GNCC). IEEE, 2018. http://dx.doi.org/10.1109/gncc42960.2018.9018904.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Pollini, Lorenzo, Marta Niccolini, and Mario Innocenti. "Experimental Evaluation of Decentralized Swarm Control Laws." In AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-6321.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Swarm guidance"

1

Dohner, J. L. A guidance and control algorithm for scent tracking micro-robotic vehicle swarms. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/573345.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!