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Готові списки джерел за темами / Swarm verification

Добірка наукової літератури з теми "Swarm verification"

Автор: Grafiati

Опубліковано: 31 травня 2023

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Статті в журналах з теми "Swarm verification":

1

Holzmann, Gerard J., Rajeev Joshi, and Alex Groce. "Swarm Verification Techniques." IEEE Transactions on Software Engineering 37, no. 6 (November 2011): 845–57. http://dx.doi.org/10.1109/tse.2010.110.

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2

LU, Nan, Xiaodong WANG, Zheng TANG, and Pei HE. "Modeling method of unmanned aerial vehicle swarm behavior based on spatiotemporal hybrid Petri net." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 4 (August 2022): 812–18. http://dx.doi.org/10.1051/jnwpu/20224040812.

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The more and more widely used UAV swarm operations have received great attention in the new global military revolution of informatization, and the integrated modeling of UAV swarms has great significance and value for the testing and verification of combat modes. Aiming at the modeling and simulation requirements of combat scenarios, taking the collaborative combat process of heterogeneous UAV swarms as the research object, starting from the modeling of a single UAV, on the basis of the formalization and mathematical description of the single combat process, this paper employs Petri nets based on the hybridization of time and space to describe the discrete states and continuous processes of heterogeneous UAV swarm systems, and effectively solves the problems of the fusion between physics and computing processes, and modeling of interactive events in swarm systems. UPPAAL is selected to formally verify the modeling of UAV swarm strike mission, which shows that the proposed modeling method is feasible and effective.

3

Wijs, Anton. "Informed Swarm Verification of Infinite-State Systems." Electronic Proceedings in Theoretical Computer Science 73 (November 11, 2011): 19. http://dx.doi.org/10.4204/eptcs.73.4.

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4

Dixon, Clare, Alan F. T. Winfield, Michael Fisher, and Chengxiu Zeng. "Towards temporal verification of swarm robotic systems." Robotics and Autonomous Systems 60, no. 11 (November 2012): 1429–41. http://dx.doi.org/10.1016/j.robot.2012.03.003.

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5

Sharmila D , A. V. Pra.bu, N. Selvaganesh,. "AUTHORSHIP VERIFICATION USING MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM." Psychology and Education Journal 58, no. 1 (January 15, 2021): 4262–66. http://dx.doi.org/10.17762/pae.v58i1.1492.

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Digital forensics is the study of recovery and investigation of the materials found in digital devices, mainly in computers. Forensic authorship analysis is a branch of digital forensics. It includes tasks such as authorship attribution, authorship verification, and author profiling. In Authorship verification, with a given a set of sample documents D written by an author A and an unknown document d, the task is to find whether document d is written by A or not. Authorship verification has been previously done using genetic algorithms, SVM classifiers, etc. The existing system creates an ensemble model by combining the features based on the similarity scores, and the parameter optimization was done using a grid search. The accuracy of verification using the grid search method is 62.14%. The time complexity is high as the system tries all possible combinations of the features during the ensemble model's construction. In the proposed work, Modified Particle Swarm Optimization (MPSO) is used to construct the classification model in the training phase, instead of the ensemble model. In addition to the combination of linguistic and character features, Average Sentence Length is used to improve the verification task accuracy. The accuracy of verification has been improved to 63.38%.

6

Huang, Ai Ming, and Mao Ling Pen. "Multi Biometrics Fusion Identity Verification Based on Particle Swarm Optimization." Applied Mechanics and Materials 44-47 (December 2010): 3195–99. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3195.

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In recent years, biometrics has become one of the most promising identity verification technologies. For the limitations, it is difficult for single mode biometrics to meet requirements of modern identity verification. The paper introduced several common biometrics verification methods and procedures. The limitations of single mode biometrics were also provided and data fusion technology was introduced to solve the problem. On the basis of this, Particle Swarm Optimization (PSO) neural network algorithm was used to construct multi biometrics verification system. The results of experiment based on the method shows that it can achieve better identity verification result and meet requirements of practical applications.

7

Wang, Chuanyun, Yang Su, Jingjing Wang, Tian Wang, and Qian Gao. "UAVSwarm Dataset: An Unmanned Aerial Vehicle Swarm Dataset for Multiple Object Tracking." Remote Sensing 14, no. 11 (May 28, 2022): 2601. http://dx.doi.org/10.3390/rs14112601.

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In recent years, with the rapid development of unmanned aerial vehicles (UAV) technology and swarm intelligence technology, hundreds of small-scale and low-cost UAV constitute swarms carry out complex combat tasks in the form of ad hoc networks, which brings great threats and challenges to low-altitude airspace defense. Security requirements for low-altitude airspace defense, using visual detection technology to detect and track incoming UAV swarms, is the premise of anti-UAV strategy. Therefore, this study first collected many UAV swarm videos and manually annotated a dataset named UAVSwarm dataset for UAV swarm detection and tracking; thirteen different scenes and more than nineteen types of UAV were recorded, including 12,598 annotated images—the number of UAV in each sequence is 3 to 23. Then, two advanced depth detection models are used as strong benchmarks, namely Faster R-CNN and YOLOX. Finally, two state-of-the-art multi-object tracking (MOT) models, GNMOT and ByteTrack, are used to conduct comprehensive tests and performance verification on the dataset and evaluation metrics. The experimental results show that the dataset has good availability, consistency, and universality. The UAVSwarm dataset can be widely used in training and testing of various UAV detection tasks and UAV swarm MOT tasks.

8

Huang, Yixin, Xiaojia Xiang, Han Zhou, Dengqing Tang, and Yihao Sun. "Online Identification-Verification-Prediction Method for Parallel System Control of UAVs." Aerospace 8, no. 4 (April 2, 2021): 99. http://dx.doi.org/10.3390/aerospace8040099.

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In order to solve the problem of how to efficiently control a large-scale swarm Unmanned Aerial Vehicle (UAV) system, which performs complex tasks with limited manpower in a non-ideal environment, this paper proposes a parallel UAV swarm control method. The key technology of parallel control is to establish a one-to-one artificial UAV system corresponding to the aerial swarm UAV on the ground. This paper focuses on the computational experiments algorithm for artificial UAV system establishment, including data processing, model identification, model verification and state prediction. Furthermore, this paper performs a comprehensive flight mission with four common modes (climbing, level flighting, turning and descending) for verification. The results of the identification experiment present a good consistency between the outputs of the refined dynamics model and the real flight data. The prediction experiment results show that the prediction method in this paper can basically guarantee that the prediction states error is kept within 10% about 16 s.

9

V. Gayetri Devi, S., C. Nalini, and N. Kumar. "An efficient software verification using multi-layered software verification tool." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 454. http://dx.doi.org/10.14419/ijet.v7i2.21.12465.

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Rapid advancements in Software Verification and Validation have been critical in the wide development of tools and techniques to identify potential Concurrent bugs and hence verify the software correctness. A concurrent program has multiple processes and shared objects. Each process is a sequential program and they use the shared objects for communication for completion of a task. The primary objective of this survey is retrospective review of different tools and methods used for the verification of real-time concurrent software. This paper describes the proposed tool ‘F-JAVA’ for multithreaded Java codebases in contrast with existing ‘FRAMA-C’ platform, which is dedicated to real-time concurrent C software analysis. The proposed system is comprised of three layers, namely Programming rules generation stage, Verification stage with Particle Swarm Optimization (PSO) algorithm, and Performance measurement stage. It aims to address some of the challenges in the verification process such as larger programs, long execution times, and false alarms or bugs, and platform independent code verification

10

Zhang, Hong, and Masumi Ishikawa. "The performance verification of an evolutionary canonical particle swarm optimizer." Neural Networks 23, no. 4 (May 2010): 510–16. http://dx.doi.org/10.1016/j.neunet.2009.12.002.

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Більше джерел

Дисертації з теми "Swarm verification":

1

Cheng, Xueqi. "Exploring Hybrid Dynamic and Static Techniques for Software Verification." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/26216.

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With the growing importance of software on which human lives increasingly depend, the correctness requirement of the underlying software becomes especially critical. However, the increasing complexities and sizes of modern software systems pose special challenges on the effectiveness as well as efficiency of software verification. Two major obstacles include the quality of test generation in terms of error detection in software testing and the state space explosion problem in software formal verification (model checking). In this dissertation, we investigate several hybrid techniques that explore dynamic (with program execution), static (without program execution) as well as the synergies of multiple approaches in software verification from the perspectives of testing and model checking. For software testing, a new simulation-based internal variable range coverage metric is proposed with the goal of enhancing the error detection capability of the generated test data when applied as the target metric. For software model checking, we utilize various dynamic analysis methods, such as data mining, swarm intelligence (ant colony optimization), to extract useful high-level information from program execution data. Despite being incomplete, dynamic program execution can still help to uncover important program structure features and variable correlations. The extracted knowledge, such as invariants in different forms, promising control flows, etc., is then used to facilitate code-level program abstraction (under-approximation/over-approximation), and/or state space partition, which in turn improve the performance of property verification. In order to validate the effectiveness of the proposed hybrid approaches, a wide range of experiments on academic and real-world programs were designed and conducted, with results compared against the original as well as the relevant verification methods. Experimental results demonstrated the effectiveness of our methods in improving the quality as well as performance of software verification. For software testing, the newly proposed coverage metric constructed based on dynamic program execution data is able to improve the quality of test cases generated in terms of mutation killing â a widely applied measurement for error detection. For software model checking, the proposed hybrid techniques greatly take advantage of the complementary benefits from both dynamic and static approaches: the lightweight dynamic techniques provide flexibility in extracting valuable high-level information that can be used to guide the scope and the direction of static reasoning process. It consequently results in significant performance improvement in software model checking. On the other hand, the static techniques guarantee the completeness of the verification results, compensating the weakness of dynamic methods.
Ph. D.

2

Puri, Prateek. "Design Validation of RTL Circuits using Binary Particle Swarm Optimization and Symbolic Execution." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/55815.

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Over the last two decades, chip design has been conducted at the register transfer (RT) Level using Hardware Descriptive Languages (HDL), such as VHDL and Verilog. The modeling at the behavioral level not only allows for better representation and understanding of the design, but also allows for encapsulation of the sub-modules as well, thus increasing productivity. Despite these benefits, validating a RTL design is not necessarily easier. Today, design validation is considered one of the most time and resource consuming aspects of hardware design. The high costs associated with late detection of bugs can be enormous. Together with stringent time to market factors, the need to guarantee the correct functionality of the design is more critical than ever. The work done in this thesis tackles the problem of RTL design validation and presents new frameworks for functional test generation. We use branch coverage as our metric to evaluate the quality of the generated test stimuli. The initial effort for test generation utilized simulation based techniques because of their scalability with design size and ease of use. However, simulation based methods work on input spaces rather than the DUT's state space and often fail to traverse very narrow search paths in large input spaces. To encounter this problem and enhance the ability of test generation framework, in the following work in this thesis, certain design semantics are statically extracted and recurrence relationships between different variables are mined. Information such as relations among variables and loops can be extremely valuable from test generation point of view. The simulation based method is hybridized with Z3 based symbolic backward execution engine with feedback among different stages. The hybridized method performs loop abstraction and is able to traverse narrow design paths without performing costly circuit analysis or explicit loop unrolling. Also structural and functional unreachable branches are identified during the process of test generation. Experimental results show that the proposed techniques are able to achieve high branch coverage on several ITC'99 benchmark circuits and their modified variants, with significant speed up and reduction in the sequence length.
Master of Science

3

Gent, Kelson Andrew. "High Quality Test Generation at the Register Transfer Level." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73544.

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Integrated circuits, from general purpose microprocessors to application specific designs (ASICs), have become ubiquitous in modern technology. As our applications have become more complex, so too have the circuits used to drive them. Moore's law predicts that the number of transistors on a chip doubles every 18-24 months. This explosion in circuit size has also lead to significant growth in testing effort required to verify the design. In order to cope with the required effort, the testing problem must be approached from several different design levels. In particular, exploiting the Register Transfer Level for test generation allows for the use of relational information unavailable at the structural level. This dissertation demonstrates several novel methods for generating tests applicable for both structural and functional tests. These testing methods allow for significantly faster test generation for functional tests as well as providing high levels of fault coverage during structural test, typically outperforming previous state of the art methods. First, a semi-formal method for functional verification is presented. The approach utilizes a SMT-based bounded model checker in combination with an ant colony optimization based search engine to generate tests with high branch coverage. Additionally, the method is utilized to identify unreachable code paths within the RTL. Compared to previous methods, the experimental results show increased levels of coverage and improved performance. Then, an ant colony optimization algorithm is used to generate high quality tests for fault coverage. By utilizing co-simulation at the RTL and gate level, tests are generated for both levels simultaneously. This method is shown to reach previously unseen levels of fault coverage with significantly lower computational effort. Additionally, the engine was also shown to be effective for behavioral level test generation. Next, an abstraction method for functional test generation is presented utilizing program slicing and data mining. The abstraction allows us to generate high quality test vectors that navigate extremely narrow paths in the state space. The method reaches previously unseen levels of coverage and is able to justify very difficult to reach control states within the circuit. Then, a new method of fault grading test vectors is introduced based on the concept of operator coverage. Operator coverage measures the behavioral coverage in each synthesizable statement in the RTL by creating a set of coverage points for each arithmetic and logical operator. The metric shows a strong relationship with fault coverage for coverage forecasting and vector comparison. Additionally, it provides significant reductions in computation time compared to other vector grading methods. Finally, the prior metric is utilized for creating a framework of automatic test pattern generation for defect coverage at the RTL. This framework provides the unique ability to automatically generate high quality test vectors for functional and defect level testing at the RTL without the need for synthesis. In summary, We present a set of tools for the analysis and test of circuits at the RTL. By leveraging information available at HDL, we can generate tests to exercise particular properties that are extremely difficult to extract at the gate level.
Ph. D.

4

Fournier, Émilien. "Accélération matérielle de la vérification de sûreté et vivacité sur des architectures reconfigurables." Electronic Thesis or Diss., Brest, École nationale supérieure de techniques avancées Bretagne, 2022. http://www.theses.fr/2022ENTA0006.

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Le Model-Checking est une technique automatisée, utilisée dans l’industrie pour la vérification, enjeu majeur pour la conception de systèmes fiables, cadre dans lequel performance et scalabilité sont critiques. La vérification swarm améliore la scalabilité par une approche partielle reposant sur l’exécution concurrente d’analyses randomisées. Les architectures reconfigurables promettent des gains de performance significatifs. Cependant, les travaux existant souffrent d’une conception monolithique qui freine l’exploration des opportunités des architectures reconfigurable. De plus, ces travaux sont limités a la verification de sûreté. Pour adapter la stratégie de vérification au problème, cette thèse propose un framework de vérification matérielle, permettant de gagner, au travers d’une architecture modulaire, une généricité sémantique et algorithmique, illustrée par l’intégration de 3 langages de spécification et de 6 algorithmes. Ce cadre architectural permet l’étude de l’efficacité des algorithmes swarm pour obtenir un cœur de vérification de sûreté scalable. Les résultats, sur un FPGA haut de gamme, montrent des gains d’un ordre de grandeur par rapport à l’état de l’art. Enfin, on propose le premier accélérateur matériel permettant la vérification des exigences de sûreté et de vivacité. Les résultats démontrent un facteur d’accélération moyen de 4875x par rapport au logiciel
Model-Checking is an automated technique used in industry for verification, a major issue in the design of reliable systems, where performance and scalability are critical. Swarm verification improves scalability through a partial approach based on concurrent execution of randomized analyses. Reconfigurable architectures promise significant performance gains. However, existing work suffers from a monolithic design that hinders the exploration of reconfigurable architecture opportunities. Moreover, these studies are limited to safety verification. To adapt the verification strategy to the problem, this thesis first proposes a hardware verification framework, allowing to gain, through a modular architecture, a semantic and algorithmic genericity, illustrated by the integration of 3 specification languages and 6 algorithms. This framework allows efficiency studies of swarm algorithms to obtain a scalable safety verification core. The results, on a high-end FPGA, show gains of an order of magnitude compared to the state-of-the-art. Finally, we propose the first hardware accelerator for safety and liveness verification. The results show an average speed-up of 4875x compared to software

5

Liu, Xuan-You, and 劉軒佑. "Simulation of Swarm Intelligence in Pollution Sources Searching and Verification of Group Flight Formation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/293xxw.

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碩士
國立中山大學
資訊工程學系研究所
107
This thesis presents a group UAV navigation system for air pollution search. The system consists of a number of unmaned aerial vehicles equipped with air pollution sensors. The communication among the drones is through the Message Queuing Telemetry Transport （MQTT） protocol. In addition, the information collected by the sensors mounted on the drones will immediately be transmitted and presented visually on the control panel. Furthermore, an improved swarm intelligence algorithm for pollution source search is proposed to allow the drones to search for multiple pollution sources more efficient. Simulation results prove that the proposed algorithm can search multiple pollution sources more efficient than PSO.

6

Chien, Shih-Hai, and 錢世海. "A Study on Calculating the Optimal Turn-on Angle of Multilevel Cascade Inverter with Particle Swarm Optimization to Reduce Total Harmonic Distortion and the Verification of Hardware-in-the-loop." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/31463317801239457041.

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碩士
國立聯合大學
電機工程學系碩士班
102
The optimal switch conduction angle to solve the cascade multilevel inverter is regarded as the research subject in this study. The voltage on the power switches of a cascade multilevel inverter could be applied to high-power environments. Nevertheless, it is necessary to use the modulation strategy to have the output waveform approach sine waves, where the harmonic optimization staircase waveform strategy in step modulation could reduce the switching frequency. However, nonlinear equations need to be solved for calculating the optimal switch conduction angle. A rapid solving algorithm is required for the real-time application. To rapidly solve the optimal switch conduction angle for the harmonic optimization staircase waveform of a cascade multilevel inverter, a modified particle swarm optimization, which combines standard particle swarm optimization and complementary particle swarm optimization, is proposed in this study, where the objective function applies the sum of squares of KKT prerequisite for satisfying the harmonic optimization. The optimization is implemented with MATLAB and FPGA hardware-in-the-loop, and Simulink is used for constructing the cascade multilevel inverter model for the simulation and analysis efficacy. Since total harmonic distortion calculation and square root calculation are avoided in the objective function and the particle velocity update is improved, the proposed method appear faster speed of convergence than standard particle swarm optimization does.

Частини книг з теми "Swarm verification":

1

Wijs, Anton. "Towards Informed Swarm Verification." In Lecture Notes in Computer Science, 422–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20398-5_30.

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2

Cavalcanti, Ana, Alvaro Miyazawa, Augusto Sampaio, Wei Li, Pedro Ribeiro, and Jon Timmis. "Modelling and Verification for Swarm Robotics." In Lecture Notes in Computer Science, 1–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98938-9_1.

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3

Konur, Savas, Clare Dixon, and Michael Fisher. "Formal Verification of Probabilistic Swarm Behaviours." In Lecture Notes in Computer Science, 440–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15461-4_42.

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4

Escalante, Hugo Jair, Manuel Montes, and Luis Villaseñor. "Particle Swarm Model Selection for Authorship Verification." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 563–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_66.

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5

Nemati, Shahla, and Mohammad Ehsan Basiri. "Particle Swarm Optimization for Feature Selection in Speaker Verification." In Applications of Evolutionary Computation, 371–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12239-2_39.

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6

Kosak, Oliver, Felix Bohn, Lennart Eing, Dennis Rall, Constantin Wanninger, Alwin Hoffmann, and Wolfgang Reif. "Swarm and Collective Capabilities for Multipotent Robot Ensembles." In Leveraging Applications of Formal Methods, Verification and Validation: Engineering Principles, 525–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61470-6_31.

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7

Dixon, Clare, Alan Winfield, and Michael Fisher. "Towards Temporal Verification of Emergent Behaviours in Swarm Robotic Systems." In Towards Autonomous Robotic Systems, 336–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23232-9_30.

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8

Piho, Paul, and Jane Hillston. "A Case Study of Policy Synthesis for Swarm Robotics." In Leveraging Applications of Formal Methods, Verification and Validation: Engineering Principles, 491–506. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61470-6_29.

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9

Kosak, Oliver, Lukas Huhn, Felix Bohn, Constantin Wanninger, Alwin Hoffmann, and Wolfgang Reif. "Maple-Swarm: Programming Collective Behavior for Ensembles by Extending HTN-Planning." In Leveraging Applications of Formal Methods, Verification and Validation: Engineering Principles, 507–24. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61470-6_30.

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10

He, Yang, Kai Qu, and Xiaokai Xia. "Simulation Verification of Cruise Missile Route Planning Based on Swarm Intelligence Algorithm." In Communications in Computer and Information Science, 549–60. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-9195-0_44.

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Тези доповідей конференцій з теми "Swarm verification":

1

Holzmann, Gerard J., Rajeev Joshi, and Alex Groce. "Swarm Verification." In 2008 23rd IEEE/ACM International Conference on Automated Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/ase.2008.9.

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2

Lomuscio, Alessio, and Edoardo Pirovano. "Verifying Fault-Tolerance in Probabilistic Swarm Systems." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/46.

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We present a method for reasoning about fault-tolerance in unbounded robotic swarms. We introduce a novel semantics that accounts for the probabilistic nature of both the swarm and possible malfunctions, as well as the unbounded nature of swarm systems. We define and interpret a variant of probabilistic linear-time temporal logic on the resulting executions, including those arising from faulty behaviour by some of the agents in the swarm. We specify the decision problem of parameterised fault-tolerance, which concerns determining whether a probabilistic specification holds under possibly faulty behaviour. We outline a verification procedure that we implement and use to study a foraging protocol from swarm robotics, and report the experimental results obtained.

3

Manshor, Siti Hakimah, Shuzlina Abdul-Rahman, Yap May Lin, Sofianita Mutalib, and Azlinah Mohamed. "Signature verification using Particle Swarm Optimisation." In 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2010. http://dx.doi.org/10.1109/socpar.2010.5686089.

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4

Hart, Shae T., Nathan J. Metzger, Maximilian E. Reese, Robert T. McDonald, Michael A. Neumann, and Christopher A. Kitts. "Robotics Simulator for Development and Verification of Swarm Behaviors." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97622.

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Abstract Swarm control strategies allow for decentralized control of many simple robots to perform collective behaviors based on local interactions. We have created a new platform for developing and exploring swarm behaviors, supporting both simulation and experimental verification. The platform, designed entirely in Simulink, provides a simple implementation with satisfactory dynamics to replicate experimental trials on Santa Clara University’s Robotic Systems Laboratory’s Low-Cost Indoor Testbed. The platform allows for seamless transitions from simulation to experimentation on the testbed. The platform is currently equipped with attract, disperse, obstacle avoidance, and go-to behaviors which have been verified both in simulation and on the experimental testbed. Additionally, a composite behavior, flocking, composed of attract and go-to behaviors is presented to demonstrate the flexibility of the simulator. Future work will expand the available behaviors to include swarm adaptive navigation behavior primitives like minima and maxima finding as well as contour and ridge following.

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Baojun, Tong, Hou Ligang, Wang Zhongchao, Wang Wensi, and Wang Jinhui. "Intelligent car platform for swarm algorithm verification." In 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). IEEE, 2017. http://dx.doi.org/10.1109/icemi.2017.8265765.

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Sauter, John A., and Kellen Bixler. "DSOARS: a swarm engineering and verification environment." In Unmanned Systems Technology XXI, edited by Charles M. Shoemaker, Paul L. Muench, and Hoa G. Nguyen. SPIE, 2019. http://dx.doi.org/10.1117/12.2518127.

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Zhang, Zhengkui, Brian Nielsen, and Kim G. Larsen. "Time optimal reachability analysis using swarm verification." In SAC 2016: Symposium on Applied Computing. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2851613.2851828.

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Fournier, Emilien, Ciprian Teodorov, and Loic Lagadec. "Dolmen: FPGA Swarm for Safety and Liveness Verification." In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2022. http://dx.doi.org/10.23919/date54114.2022.9774528.

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Yazdani, Fereshte, and Mehran Emadi Andani. "Verification based on palm vein by estimating wavelet coefficient with autoregressive model." In 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC). IEEE, 2017. http://dx.doi.org/10.1109/csiec.2017.7940166.

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Fournier, Emilien, Ciprian Teodorov, and Loic Lagadec. "Carnac: Algorithm Variability for Fast Swarm Verification on FPGA." In 2021 31st International Conference on Field-Programmable Logic and Applications (FPL). IEEE, 2021. http://dx.doi.org/10.1109/fpl53798.2021.00038.

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