Journal articles: '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

11

Li, Shuman, Chao Li, Liyang Xu, Wenjing Yang, and Xucan Chen. "Numerical Simulation and Analysis of Fish-Like Robots Swarm." Applied Sciences 9, no. 8 (April 21, 2019): 1652. http://dx.doi.org/10.3390/app9081652.

Full text

Abstract:

Artificial fish-like robot is an important branch of underwater robot research. At present, most of fish-like robot research focuses on single robot mechanism behavior, some research pays attention to the influence of the hydro-environment on robot crowds but does not reach a unified conclusion on the efficiency of fish-like robots swarm. In this work, the fish-like robots swarm is studied by numerical simulation. Four different formations, including the tandem, the phalanx, the diamond, and the rectangle are conducted by changing the spacing between fishes. The results show that at close spacing, the fish in the back can obtain a large wake from the front fish, but suffers large lateral power loss from the lateral fish. On the contrary, when the spacing is large, both the wake and pressure caused by the front and side fishes become small. In terms of the average swimming efficiency of fish swarms, we find that when the fish spacing is less than 1.25 L (L is the length of the fish body), the tandem swarm is the best choice. When the spacing is 1.25 L , the tandem, diamond and rectangle swarms have similar efficiency. When the spacing is larger than 1.25 L , the rectangle swarm is more efficient than other formations. The findings will provide significant guidance for the control of fish-like robots swarm.

APA, Harvard, Vancouver, ISO, and other styles

12

Hogg, Elliott, Sabine Hauert, David Harvey, and Arthur Richards. "Evolving behaviour trees for supervisory control of robot swarms." Artificial Life and Robotics 25, no. 4 (October 18, 2020): 569–77. http://dx.doi.org/10.1007/s10015-020-00650-2.

Full text

Abstract:

Abstract Supervisory control of swarms is essential to their deployment in real-world scenarios to both monitor their operation and provide guidance. We explore mechanisms by which humans can provide supervisory control to swarms to improve their performance. Rather than have humans guess the correct form of supervisory control, we use artificial evolution to learn effective human-readable strategies. Behaviour trees are applied to represent human-readable decision strategies which are produced through evolution. These strategies can be thoroughly tested and can provide knowledge to be used in the future in a variety of scenarios. A simulated set of scenarios are investigated where a swarm of robots have to explore varying environments and reach sets of objectives. Effective supervisory control strategies are evolved to explore each environment using different local swarm behaviours. The evolved behaviour trees are examined in detail alongside swarm simulations to enable clear understanding of the supervisory strategies. We conclude by identifying the strengths in accelerated testing and the benefits of this approach for scenario exploration and training of human operators.

APA, Harvard, Vancouver, ISO, and other styles

13

He, Fan, Weiyi Chen, and Yang Bao. "Novel Particle Swarm Optimization Guidance for Hypersonic Target Interception with Impact Angle Constraint." International Journal of Aerospace Engineering 2022 (August 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/7615644.

Full text

Abstract:

Considering that the terminal impact angle constraint can improve the interception performance of hypersonic target, a novel particle swarm optimization guidance (NPSOG) algorithm is proposed to satisfy the impact angle constraint. Two-dimensional dynamics engagement mode for hypersonic target interception is formulated. The performance index is positively correlated with the line-of-sight (LOS), LOS rate, and the relative distance between missile and target. The weight coefficients among the three are adaptively adjusted by the fuzzy logic controller. The particle swarm optimization (PSO) algorithm is utilized to generate the guidance commands. Numerical examples are given to verify the performance of the proposed guidance law in various engagement scenarios, and the performance of the algorithm is validated comparing with several heuristic guidance methods and nonheuristic guidance methods.

APA, Harvard, Vancouver, ISO, and other styles

14

El-Fiqi, Heba, Benjamin Campbell, Saber Elsayed, Anthony Perry, Hemant Kumar Singh, Robert Hunjet, and Hussein A. Abbass. "The Limits of Reactive Shepherding Approaches for Swarm Guidance." IEEE Access 8 (2020): 214658–71. http://dx.doi.org/10.1109/access.2020.3037325.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Jang, Inmo, Hyo-Sang Shin, and Antonios Tsourdos. "Local information-based control for probabilistic swarm distribution guidance." Swarm Intelligence 12, no. 4 (November 16, 2018): 327–59. http://dx.doi.org/10.1007/s11721-018-0160-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Dong, Na, Xing Fang, and Ai-guo Wu. "A Novel Chaotic Particle Swarm Optimization Algorithm for Parking Space Guidance." Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/5126808.

Full text

Abstract:

An evolutionary approach of parking space guidance based upon a novel Chaotic Particle Swarm Optimization (CPSO) algorithm is proposed. In the newly proposed CPSO algorithm, the chaotic dynamics is combined into the position updating rules of Particle Swarm Optimization to improve the diversity of solutions and to avoid being trapped in the local optima. This novel approach, that combines the strengths of Particle Swarm Optimization and chaotic dynamics, is then applied into the route optimization (RO) problem of parking lots, which is an important issue in the management systems of large-scale parking lots. It is used to find out the optimized paths between any source and destination nodes in the route network. Route optimization problems based on real parking lots are introduced for analyzing and the effectiveness and practicability of this novel optimization algorithm for parking space guidance have been verified through the application results.

APA, Harvard, Vancouver, ISO, and other styles

17

Lim, Kian Sheng, Zuwairie Ibrahim, Salinda Buyamin, Anita Ahmad, Faradila Naim, Kamarul Hawari Ghazali, and Norrima Mokhtar. "Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions." Scientific World Journal 2013 (2013): 1–19. http://dx.doi.org/10.1155/2013/510763.

Full text

Abstract:

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

APA, Harvard, Vancouver, ISO, and other styles

18

Sana, Khurram Shahzad, and Weiduo Hu. "Reentry guidance by accelerated fractional-order particle swarm optimization method." Aircraft Engineering and Aerospace Technology 92, no. 8 (July 22, 2020): 1281–93. http://dx.doi.org/10.1108/aeat-11-2019-0221.

Full text

Abstract:

Purpose The aim of this study is to design a guidance method to generate a smoother and feasible gliding reentry trajectory, a highly constrained problem by formalizing the control variables profile. Design/methodology/approach A novel accelerated fractional-order particle swarm optimization (FAPSO) method is proposed for velocity updates to design the guidance method for gliding reentry flight vehicles with fixed final energy. Findings By using the common aero vehicle as a test case for the simulation purpose, it is found that during the initial phase of the longitudinal guidance, there are oscillations in the state parameters which cause to violate the path constraints. For the glide phase of the longitudinal guidance, the path constraints have higher values because of the increase in the atmosphere density. Research limitations/implications The violation in the path constraints may compromise the flight vehicle safety, whereas the enforcement assures the flight safety by flying it within the reentry corridor. Originality/value An oscillation suppression scheme is proposed by using the FAPSO method during the initial phase of the reentry flight, which smooths the trajectory and enforces the path constraints partially. To enforce the path constraints strictly in the glide phase, ultimately, another scheme by using the FAPSO method is proposed. The simulation results show that the proposed algorithm is efficient to achieve better convergence and accuracy for nominal as well as dispersed conditions.

APA, Harvard, Vancouver, ISO, and other styles

19

Hassan, Labeed, Sayed Hosain Sadati, and Jalal Karimi. "An Optimal Fuzzy Logic Guidance Law using Particle Swarm Optimization." International Journal of Computer Applications 69, no. 3 (May 17, 2013): 40–47. http://dx.doi.org/10.5120/11825-7526.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

LIN, DeFu, BoYa LI, ShaoMing HE, and Jiang WANG. "On virtual leader-follower-based distributed cooperative swarm guidance strategy." SCIENTIA SINICA Technologica 50, no. 5 (April 15, 2020): 506–15. http://dx.doi.org/10.1360/sst-2019-0265.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Wong, Sai-Keung, Pao-Kun Tang, Fu-Shun Li, Zong-Min Wang, and Shih-Ting Yu. "Guidance path scheduling using particle swarm optimization in crowd simulation." Computer Animation and Virtual Worlds 26, no. 3-4 (April 29, 2015): 387–95. http://dx.doi.org/10.1002/cav.1636.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Zong, Xinlu, Jingxi Yi, Chunzhi Wang, Zhiwei Ye, and Naixue Xiong. "An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks." Electronics 11, no. 4 (February 18, 2022): 649. http://dx.doi.org/10.3390/electronics11040649.

Full text

Abstract:

A two-layer artificial fish swarm evacuation model based on heterogeneous pheromones is presented in this paper. Firstly, the movements of evacuees are simulated by the behaviors of an artificial fish swarm, including preying, swarming, and following. Then, the positive feedback mechanism of heterogeneous pheromones is introduced to improve evacuation performance. Based on the interaction and communication mechanisms of biological groups of social networks in nature, the perceptual and cooperative model among individuals and between individuals and the environment is established. An optimization scheme based on fish swarms and heterogeneous pheromones is proposed. The simulation and experimental results show that the two-layer evacuation model can optimize the spatial-temporal distribution of people and can finally achieve better evacuation plans. The proposed model and algorithm can provide effective guidance for emergency safety responses and robot cooperative control in intelligent robot systems.

APA, Harvard, Vancouver, ISO, and other styles

23

Bassolillo, Salvatore Rosario, Egidio D’Amato, Immacolata Notaro, Luciano Blasi, and Massimiliano Mattei. "Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs." Sensors 20, no. 15 (August 3, 2020): 4324. http://dx.doi.org/10.3390/s20154324.

Full text

Abstract:

This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft.

APA, Harvard, Vancouver, ISO, and other styles

24

Niu, Liyong, and Di Zhang. "Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization." Scientific World Journal 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/354952.

Full text

Abstract:

Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly.

APA, Harvard, Vancouver, ISO, and other styles

25

Kim, Su-Rim, Hyun-Jae Jo, Jung-Hyeon Kim, and Jong-Yong Park. "Formation Control of Swarming Vessels Using a Virtual Matrix Approach and ISOT Guidance Algorithm." Processes 9, no. 9 (September 3, 2021): 1581. http://dx.doi.org/10.3390/pr9091581.

Full text

Abstract:

The formation control for the effective operation of multiple vessels is discussed. First, a virtual matrix approach is proposed to improve the formation robustness and transform performance during swarm operations, which is created based on the virtual leader vessel location, and agents composing the formation follow cells in the matrix to maintain formation. This approach is affected by the virtual leader vessel location. The virtual leader vessel location is defined by two cases: matrix center and geometric center; furthermore, robustness and efficiency comparison simulations are performed. The simulation results show that in most formations, the geometric center is better in terms of efficiency and robustness. Second, the isosceles triangle guidance algorithm is proposed to improve the “go-back behavior” of certain agents during excessive maneuvering. Through a waypoint-following simulation, the algorithm is confirmed to be superior to the line-of-sight guidance algorithm. The swarm simulation on the virtual map verifies the performance of the proposed formation control and guidance algorithm.

APA, Harvard, Vancouver, ISO, and other styles

26

Kachroudi, Sofiene, Mathieu Grossard, and Neil Abroug. "Predictive Driving Guidance of Full Electric Vehicles Using Particle Swarm Optimization." IEEE Transactions on Vehicular Technology 61, no. 9 (November 2012): 3909–19. http://dx.doi.org/10.1109/tvt.2012.2212735.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Zhou, Huan, Yintong Li, and Tong Han. "Anticollision Decision and Control of UAV Swarm Based on Intelligent Cognitive Game." Computational Intelligence and Neuroscience 2022 (August 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/6398039.

Full text

Abstract:

UAV swarm anticollision system is very important to improve the flight safety of the whole swarm formation, while the existing system design methods are still insufficient in realizing autonomous and cooperative anticollision. Based on the cognitive game theory, an intelligent decision-making and control method for UAV swarm anticollision is designed. Firstly, by using the idea of swarm intelligence, basic flight behaviors of UAV swarm are defined as five basic flight rules, such as cohesion, following, self-guidance, dispersion, and alliance. Further, the cognitive security domain of UAV swarm is constructed by setting the overall anticollision rules of the swarm and the anticollision rules of individual members. On this basis, the anticollision problem of UAV swarm is transformed into a game problem involving two parties, and the solution method of decision and control strategy set is proposed. Finally, the stability of anticollision decision and control method is proved through eigenvalue theory. The simulation results show that the method proposed in this paper can effectively realize the autonomous cooperative anticollision of UAV swarm and also has good algorithm real-time solution ability while ensuring flight safety.

APA, Harvard, Vancouver, ISO, and other styles

28

Singh, Yogang, Marco Bibuli, Enrica Zereik, Sanjay Sharma, Asiya Khan, and Robert Sutton. "A Novel Double Layered Hybrid Multi-Robot Framework for Guidance and Navigation of Unmanned Surface Vehicles in a Practical Maritime Environment." Journal of Marine Science and Engineering 8, no. 9 (August 19, 2020): 624. http://dx.doi.org/10.3390/jmse8090624.

Full text

Abstract:

Formation control and cooperative motion planning are two major research areas currently being used in multi robot motion planning and coordination. The current study proposes a hybrid framework for guidance and navigation of swarm of unmanned surface vehicles (USVs) by combining the key characteristics of formation control and cooperative motion planning. In this framework, two layers of offline planning and online planning are integrated and applied on a practical marine environment. In offline planning, an optimal path is generated from a constrained A* path planning approach, which is later smoothed using a spline. This optimal trajectory is fed as an input for the online planning where virtual target (VT) based multi-agent guidance framework is used to navigate the swarm of USVs. This VT approach combined with a potential theory based swarm aggregation technique provides a robust methodology of global and local collision avoidance based on known positions of the USVs. The combined approach is evaluated with the different number of USVs to understand the effectiveness of the approach from the perspective of practicality, safety and robustness.

APA, Harvard, Vancouver, ISO, and other styles

29

Cong, Mingyu, Xianghong Cheng, Zhiquan Zhao, and Zhijun Li. "Studies on Multi-Constraints Cooperative Guidance Method Based on Distributed MPC for Multi-Missiles." Applied Sciences 11, no. 22 (November 17, 2021): 10857. http://dx.doi.org/10.3390/app112210857.

Full text

Abstract:

Cooperative terminal guidance with impact angle constraint is a key technology to achieve a saturation attack and improve combat effectiveness. The present study envisaged cooperative terminal guidance with impact angle constraint for multiple missiles. In this pursuit, initially, the three-dimensional cooperative terminal guidance law with multiple constraints was studied. The impact time cooperative strategy of virtual leader missile and follower missiles was designed by introducing virtual leader missiles. Subsequently, based on the distributed model prediction control combined with the particle swarm optimization algorithm, a cooperative terminal guidance algorithm was designed for multiple missiles with impact angle constraint that met the guidance accuracy. Finally, the effectiveness of the algorithm was verified using simulation experiments.

APA, Harvard, Vancouver, ISO, and other styles

30

Yin, Peng-Yeng, Fred Glover, Manuel Laguna, and Jia-Xian Zhu. "A Complementary Cyber Swarm Algorithm." International Journal of Swarm Intelligence Research 2, no. 2 (April 2011): 22–41. http://dx.doi.org/10.4018/jsir.2011040102.

Full text

Abstract:

A recent study (Yin et al., 2010) showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the tabu search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to the Standard PSO 2007 method (Clerc, 2008).

APA, Harvard, Vancouver, ISO, and other styles

31

Liu, JH, JY Shan, and Q. Liu. "Optimal pulsed guidance law with terminal impact angle constraint." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 11 (August 28, 2016): 1993–2005. http://dx.doi.org/10.1177/0954410016664918.

Full text

Abstract:

An optimal pulsed guidance law with a time-varying weighted quadratic cost function that enables imposing a predetermined intercept angle is presented. Due to the characteristic of impulse force, admissible variance of control is redefined. The optimal pulsed guidance law is deduced via extended maximum principle. The optimal pulsed guidance law is eventually transformed to solve the two-point boundary value problem. To decide a shooting point, an efficient algorithm is proposed by combining particle swarm optimization and Kriging surrogate model method. The optimal pulsed guidance law is implemented in several representative engagements. From simulation results, it can be seen that the proposed guidance law can achieve small miss distance with terminal impact angle constraint under different conditions. Moreover, the performance of the proposed guidance law is satisfactory with the comparison of sliding-mode pulsed guidance law.

APA, Harvard, Vancouver, ISO, and other styles

32

Sun, Yuefang, Kangkang Jin, Zhaozhuang Guo, Chen Zhang, and Hao Wang. "Research on Intelligent Guidance Optimal Path of Shared Car Charging in the IOT Environment." Wireless Communications and Mobile Computing 2020 (April 24, 2020): 1–13. http://dx.doi.org/10.1155/2020/3714879.

Full text

Abstract:

In recent years, with the improvement of Internet of Things (IOT) technology, a “shared” service concept has appeared in people’s life. In the limited available resources, it is of great value to study the optimal path of charging pile selection for shared cars. With the help of Internet of Things technology and through analyzing the collected data, this paper introduces three path optimization methods, the Dijkstra algorithm, heuristic algorithm A∗, and improved particle swarm optimization (PSO) algorithm; establishes relevant convergence conditions; and takes the actual path cost as the criterion to judge the optimal path. In addition, this paper studies the optimal path from the shared car to the charging pile. Through the simulation experiment, the results show that compared with the traditional optimal path algorithm, the improved particle swarm optimization algorithm has strong parallelism and better search effect for optimal path selection in the case of large number of traffic path nodes and complex paths, which fully reflects the performance advantage of the algorithm.

APA, Harvard, Vancouver, ISO, and other styles

33

Bernardi, Sara, and Annachiara Colombi. "A particle model reproducing the effect of a conflicting flight information on the honeybee swarm guidance." Communications in Applied and Industrial Mathematics 9, no. 1 (December 1, 2018): 159–73. http://dx.doi.org/10.2478/caim-2018-0021.

Full text

Abstract:

Abstract The honeybee swarming process is steered by few scout individuals, which are the unique informed on the location of the target destination. Theoretical and experimental results suggest that bee coordinated flight arises from visual signals. However, how the information is passed within the population is still debated. Moreover, it has been observed that honeybees are highly sensitive to conflicting directional information. In fact, swarms exposed to fast-moving bees headed in the wrong direction show clear signs of disrupted guidance. In this respect, we here present a discrete mathematical model to investigate different hypotheses on the behaviour both of informed and uninformed bees. In this perspective, numerical realizations, specifically designed to mimic selected experiments, reveal that only one combination of the considered assumptions is able to reproduce the empirical outcomes, resulting thereby the most reliable mechanism underlying the swarm dynamics according to the proposed approach. Specifically, this study suggests that (i) leaders indicate the right flight direction by repeatedly streaking at high speed pointing towards the target and then slowly coming back to the trailing edge of the bee cloud; and (ii) uninformed bees, in turn, gather the route information by adapting their movement to all the bees sufficiently close to their position.

APA, Harvard, Vancouver, ISO, and other styles

34

Yan, Zheping, Jiyun Li, Yi Wu, and Gengshi Zhang. "A Real-Time Path Planning Algorithm for AUV in Unknown Underwater Environment Based on Combining PSO and Waypoint Guidance." Sensors 19, no. 1 (December 21, 2018): 20. http://dx.doi.org/10.3390/s19010020.

Full text

Abstract:

It is a challengeable task to plan multi-objective optimization paths for autonomous underwater vehicles (AUVs) in an unknown environments, which involves reducing travel time, shortening path length, keeping navigation safety, and smoothing trajectory. To address the above challenges, a real-time path planning approach combining particle swarm optimization and waypoint guidance is proposed for AUV in unknown oceanic environments in this paper. In this algorithm, a multi-beam forward looking sonar (FLS) is utilized to detect obstacles and the output data of FLS are used to produce those obstacles’ outlines (polygons). Particle swarm optimization is used to search for appropriate temporary waypoints, in which the optimization parameters of path planning are taken into account. Subsequently, an optimal path is automatically generated under the guidance of the destination and these temporary waypoints. Finally, three algorithms, including artificial potential field and genic algorithm, are adopted in the simulation experiments. The simulation results show that the proposed algorithm can generate the optimal paths compared with the other two algorithms.

APA, Harvard, Vancouver, ISO, and other styles

35

Jia, Ying-Hui, Jun Qiu, Zhuang-Zhuang Ma, and Fang-Fang Li. "A Novel Crow Swarm Optimization Algorithm (CSO) Coupling Particle Swarm Optimization (PSO) and Crow Search Algorithm (CSA)." Computational Intelligence and Neuroscience 2021 (May 22, 2021): 1–14. http://dx.doi.org/10.1155/2021/6686826.

Full text

Abstract:

The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems.

APA, Harvard, Vancouver, ISO, and other styles

36

Li, Xuewei, Miao Gao, Zhen Kang, Xiangyu Chen, Xi Zeng, Shuai Chen, Haixin Sun, and Anmin Zhang. "Cooperative Path Tracking for Swarm of MASSs Based on Consensus Theory." Journal of Marine Science and Engineering 11, no. 2 (February 1, 2023): 312. http://dx.doi.org/10.3390/jmse11020312.

Full text

Abstract:

At present, marine autonomous surface ships (MASSs) play a huge role in marine shipping, surveying and mapping, safeguarding the rights and interests of marine space, and other maritime tasks. The cooperative operation of a swarm of MASSs can extend the scope of operation of the MASSs, and thus allow them to carry out more complex tasks. Path tracking is an important problem for the control of a swarm of MASSs. In this paper, the control of underactuated MASSs is decoupled, to control the heading and speed, respectively. First of all, in the path tracking, the improved arc LOS guidance law is introduced, and the heading torque controller is designed, so that the MASS can track the reference path efficiently and accurately. Then the single-path guided path tracking without formation of the swarm of MASSs is studied, the reference path of the swarm center tracking is defined, and the heave thrust controller of the swarm of MASSs is designed based on consensus theory, so that the surge velocity of the MASS can tend towards consistentcy, and finally converge to the desired speed. Finally, the effectiveness of the proposed control strategy is verified by two groups of simulation experiments.

APA, Harvard, Vancouver, ISO, and other styles

37

Hassan, Labeed, Seyed Hossein Sadati, Mohamad Bagher Malaeak, Mohamad Ali Ashtiani, and Jalal Karimi. "A New Optimal Fuzzy Logic Guidance Law using Time Variant Particle Swarm Optimization." International Journal of Computer Applications 72, no. 7 (June 26, 2013): 34–37. http://dx.doi.org/10.5120/12508-9121.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Zhou, Xuanying, Zhengming Wang, Dong Li, Haiyin Zhou, Yongrui Qin, and Jiongqi Wang. "Guidance Systematic Error Separation for Mobile Launch Vehicles Using Artificial Fish Swarm Algorithm." IEEE Access 7 (2019): 31422–34. http://dx.doi.org/10.1109/access.2019.2893765.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Long, Nathan K., Karl Sammut, Daniel Sgarioto, Matthew Garratt, and Hussein A. Abbass. "A Comprehensive Review of Shepherding as a Bio-Inspired Swarm-Robotics Guidance Approach." IEEE Transactions on Emerging Topics in Computational Intelligence 4, no. 4 (August 2020): 523–37. http://dx.doi.org/10.1109/tetci.2020.2992778.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Tartaglione, Gaetano, Marco Ariola, Egidio D’Amato, and Pierluigi Salvo Rossi. "A 3D Decentralized Guidance and Control System for a Swarm of Multi-Copters." IFAC-PapersOnLine 50, no. 1 (July 2017): 5788–93. http://dx.doi.org/10.1016/j.ifacol.2017.08.424.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Zhou, Hongyu, Xiaogang Wang, Bing Bai, and Naigang Cui. "Reentry guidance with constrained impact for hypersonic weapon by novel particle swarm optimization." Aerospace Science and Technology 78 (July 2018): 205–13. http://dx.doi.org/10.1016/j.ast.2018.04.024.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Li, Zhi, Guihe Chen, and Feng Wang. "Optimization of Esophageal Ultrasound under Artificial Fish Swarm Algorithm and Its Adoption in Treatment of Ventricular Septal Defect." Scientific Programming 2021 (November 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/7126251.

Full text

Abstract:

This work was aimed at exploring the adoption value of the optimized and upgraded esophageal ultrasound in the treatment of patients with ventricular septal defect (VSD) by artificial fish swarm algorithm. A model was built based on artificial fish swarm algorithm. A random ultrasonic optical signal in the database was decomposed several times and sparsity was optimized to complete partial optimization, which was then extended to global optimization. A total of 100 patients with ventricular septal defect were divided into control group who underwent cardiopulmonary bypass under the guidance of three-dimensional thoracic ultrasound and experimental group of ventricular septal defect occlusion under the guidance of esophageal ultrasound based on artificial fish swarm algorithm. The results showed that the number of successful cases in the experimental group was 12 cases of perimembranous type, 10 cases of septal type, 7 cases of simple membranous type, 13 cases of muscular type, 4 cases of subdry type, and 2 cases of ridge type. The average length of operation after surgery was 70.65 minutes, the average length of ventilator ventilation was 125.8 minutes, and the average length of intensive care unit was 377.9 minutes. The average length of hospital stay after surgery was 5.6 days, and the average total length of hospital stay was 8.2 days, which were better than the control group in many aspects, with statistical significance ( P < 0.05 ). In short, the artificial fish swarm algorithm for esophageal ultrasound-guided ventricular septal defect closure had short operation time and good postoperative effect, which was of high application value in the clinical treatment of patients with ventricular septal defect.

APA, Harvard, Vancouver, ISO, and other styles

43

Gai, Wendong, Ning Zhang, Jing Zhang, and Yuxia Li. "A constant guidance law-based collision avoidance for unmanned aerial vehicles." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 4 (January 9, 2018): 1204–16. http://dx.doi.org/10.1177/0954410017751325.

Full text

Abstract:

A method of automatic collision avoidance based on constant guidance law is proposed to solve the problem that the collision avoidance time is difficult to be estimated accurately during the collision avoidance process. The guidance command of the collision avoidance method is constant, and the lower bound of the constant guidance command satisfying the safe collision avoidance requirement is given by geometric method. Then, a collision avoidance time estimation method based on particle swarm optimization is proposed. In addition, the attitude control loop adopts the nonlinear proportional-integral-derivative based on tracking-differentiator, and the sufficient conditions for the system stability are given using the T-passive method. The simulation results show that the collision avoidance time estimation error is small and the maneuvering range of collision avoidance is smaller than the collision avoidance method based on proportional guidance and nonlinear dynamic inverse guidance. This method can achieve collision avoidance under the influence of wind disturbance and safety distance abrupt change.

APA, Harvard, Vancouver, ISO, and other styles

44

Li, Sining, and Kai Chen. "Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization." Computational Intelligence and Neuroscience 2022 (October 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/5055338.

Full text

Abstract:

Aiming at the problems of single planning technology and relatively few resource types in the process of regional comprehensive energy system planning at the current stage, this paper proposes a method based on the construction of regional comprehensive energy system planning and the operation model. Through the integration of the particle swarm optimization algorithm, this method can better realize the optimization and economic operation of the regional comprehensive energy system and build a system optimization mode based on two stages of planning and operation to pursue the optimal configuration of system equipment. Through the simulation algorithm, it is found that the solution time of the traditional basic particle swarm optimization algorithm is 10.49s, while the average solution time of the particle swarm optimization system proposed in this study is 7.93s; the efficiency is increased by 24.4%, and the system operation efficiency is significantly improved, providing theoretical and technical guidance for the economic operation of the regional comprehensive energy system.

APA, Harvard, Vancouver, ISO, and other styles

45

Banks, Alec, Jonathan Vincent, and Keith Phalp. "Particle Swarm Guidance System for Autonomous Unmanned Aerial Vehicles in an Air Defence Role." Journal of Navigation 61, no. 1 (December 10, 2007): 9–29. http://dx.doi.org/10.1017/s0373463307004444.

Full text

Abstract:

This work investigates the utilisation of Particle Swarm Optimisation (PSO) for the non-deterministic navigation of Unmanned Aerial Vehicles (UAVs), allowing them to work cooperatively toward the goal of protecting a wide area against airborne attack. To negate the PSO's inherent weakness in dynamic environments, a neighbourhood scheme is proposed that not only enables the efficient interception of targets several times faster than the UAVs but also facilitates the maintenance of effective airspace coverage. Empirical results suggest that these techniques may indeed be of use in autonomous navigation systems for UAVs in air defence roles.

APA, Harvard, Vancouver, ISO, and other styles

46

Jeong, Junho, Hyunsam Myung, Dowan Kim, and Heungsik Lim. "Design of Decentralized Guidance Algorithm for Swarm Flight of Fixed-Wing Unmanned Aerial Vehicles." Journal of the Korean Society for Aeronautical & Space Sciences 49, no. 12 (December 31, 2021): 981–88. http://dx.doi.org/10.5139/jksas.2021.49.12.981.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Guangwei Zhao, Yongquan Zhou, and Yingju Wang. "Using Complex Method Guidance GSO Swarm Algorithm for Solving High Dimensional Function Optimization Problem." Journal of Convergence Information Technology 6, no. 11 (November 30, 2011): 352–60. http://dx.doi.org/10.4156/jcit.vol6.issue11.40.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Guo, Weian, Chengyong Si, Yu Xue, Yanfen Mao, Lei Wang, and Qidi Wu. "A Grouping Particle Swarm Optimizer with Personal-Best-Position Guidance for Large Scale Optimization." IEEE/ACM Transactions on Computational Biology and Bioinformatics 15, no. 6 (November 1, 2018): 1904–15. http://dx.doi.org/10.1109/tcbb.2017.2701367.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Chen, Kuei-Yi, Yung-Lung Lee, Sheng-Ju Liao, and Chien-Chun Kung. "The design of particle swarm optimization guidance using a line-of-sight evaluation method." Computers & Electrical Engineering 54 (August 2016): 159–69. http://dx.doi.org/10.1016/j.compeleceng.2016.01.023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Han, Bao Ru, Jing Bing Li, and Heng Yu Wu. "Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on Particle Swarm Neural Network." Advanced Materials Research 712-715 (June 2013): 1965–69. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.1965.

Full text

Abstract:

This paper presents a tolerance analog circuit hard fault and soft fault diagnosis method based on the BP neural network and particle swarm optimization algorithm. First, select the mean square error function of BP neural network as the fitness function of the PSO algorithm. Second, change the guidance of neural network algorithms rely on gradient information to adjust the network weights and threshold methods, through the use of the characteristics of the particle swarm algorithm groups parallel search to find more appropriate network weights and threshold. Then using the adaptive learning rate and momentum BP algorithm to train the BP neural network. Finally, the network is applied to fault diagnosis of analog circuit, can quickly and effectively to the circuit fault diagnosis.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Swarm guidance'

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