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ARCog: An Aerial Robotics Cognitive Architecture

Published online by Cambridge University Press:  16 July 2020

Milena F. Pinto ,
Leonardo M. Honório ,
Andre L. M. Marcato Open the ORCID record for Andre L. M. Marcato [Opens in a new window] ,
Mario A. R. Dantas ,
Aurelio G. Melo Open the ORCID record for Aurelio G. Melo [Opens in a new window] ,
Miriam Capretz  and
Cristina Urdiales
Milena F. Pinto*
Affiliation:
Department of Electronics, Federal Center for Technological Education of Rio de Janeiro (CEFET-RJ), Rio de Janeiro, Brazil
Leonardo M. Honório
Affiliation:
Department of Electrical Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora, Brazil
Andre L. M. Marcato
Affiliation:
Department of Electrical Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora, Brazil
Mario A. R. Dantas
Affiliation:
Department of Electrical Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora, Brazil
Aurelio G. Melo
Affiliation:
Department of Electrical Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora, Brazil
Miriam Capretz
Affiliation:
Department of Electrical and Computer Engineering, Faculty of Engineering, Western University, London, Canada
Cristina Urdiales
Affiliation:
Department of Electronics Technology, University of Málaga, Málaga, Spain
*
*Corresponding author. E-mail: milenafaria@ieee.org
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Summary

Efficient algorithm integration is a key issue in aerial robotics. However, only a few integration solutions rely on a cognitive approach. Cognitive approaches break down complex problems into independent units that may deal with progressively lower-level data interfaces, all the way down to sensors and actuators. A cognitive architecture defines information flow among units to produce emergent intelligent behavior. Despite the improvements in autonomous decision-making, several key issues remain open. One of these issues is the selection, coordination, and decision-making related to the several specialized tasks required for fulfilling mission objectives. This work addresses decision-making for the cognitive unmanned-aerial-vehicle architecture coined as ARCog. The proposed architecture lays the groundwork for the development of a software platform aligned with the requirements of the state-of-the-art technology in the field. The system is designed to provide high-level decision-making. Experiments prove that ARCog works correctly in its target scenario.

Keywords

Unmanned aerial vehicleIntelligent systemsAutonomous missionDecentralized architectureDecision-making
Type
Articles
Information
Robotica , Volume 39 , Issue 3 , March 2021 , pp. 483 - 502
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Selecký, M., Rollo, M., Losiewicz, P., Reade, J. and Maida, N., “Framework for Incremental Development of Complex Unmanned Aircraft Systems,” Integrated Communication, Navigation, and Surveillance Conference (ICNS), 2015 (IEEE, 2015) pp. J3–1.CrossRefGoogle Scholar
Du, Y., de Silva, C. W. and Liu, D., “A Multi-agent Hybrid Cognitive Architecture with Self-awareness for Homecare Robot,” 2014 9th International Conference on Computer Science & Education (ICCSE) (IEEE, 2014) pp. 223–228.Google Scholar
Pérez-Ortiz, M., Peña, J. M., Gutiérrez, P. A., Torres-Sánchez, J., Hervás-Martnez, C. and López-Granados, F., “Selecting patterns and features for between- and within- crop-row weed mapping using uav-imagery,” Expert Syst. Appl. 47, 85–94 (2016).CrossRefGoogle Scholar
Ropero, F., P. Muñoz and M. D. R-Moreno, “Terra: A path planning algorithm for cooperative UGV-UAV exploration,” Eng. Appl. Artif. Intell. 78, 260–272 (2019).CrossRefGoogle Scholar
Sun, X., Cai, C., Yang, J. and Shen, X., “Route evaluation for unmanned aerial vehicle based on type-2 fuzzy sets,” Eng. Appl. Artif. Intell. 39, 132–145 (2015).CrossRefGoogle Scholar
Sanchez-Lopez, J. L., Molina, M., Bavle, H., Sampedro, C., Fernández, R. A. S. and Campoy, P., “A multi-layered component-based approach for the development of aerial robotic systems: The aerostack framework,” J. Intell. Robot. Syst. 88(2–4), 683–709 (2017).CrossRefGoogle Scholar
Van Dang, C., Tran, T. T., Shin, Y.-B., Gil, K.-J., Ngo, D., Kang, B.-S., Lee, G.-D. and Kim, J.-W., “Application of a soar-ROS agent on finding the correct object,” International Conference on Advanced Science and Technology and Advanced Signal Processing, Hanoi, Vietnam, (2017) vol. 143, pp. 151–155.Google Scholar
Gobet, F. and Lane, P. C., “Constructing a standard model: Lessons from chrest,” 2017 AAAI Fall Symposium Series (2017).Google Scholar
Insaurralde, C. C., “Service-oriented agent architecture for unmanned air vehicles,” 2014 IEEE/AIAA 33rd Digital Avionics Systems Conference (DASC) (IEEE, 2014) pp. 8B1–1.CrossRefGoogle Scholar
Ball, J. T., “Advantages of ACT-R over Prolog for Natural Language Analysis,” Proceedings of the 22nd Annual Conference on Behavior Representation in Modeling and Simulation (Citeseer, 2013).Google Scholar
Trafton, J. G., Hiatt, L. M., Harrison, A. M., F. P. Tamborello II, S. S. Khemlani and A. C. Schultz, “ACT-R/E: An embodied cognitive architecture for human-robot interaction,” J. Human-Robot Interact. 2(1), 30–55 (2013).CrossRefGoogle Scholar
Tweedale, J. W., “A review of cognitive decision-making within future mission systems,” Procedia Comput. Sci. 35, 1043–1052 (2014).CrossRefGoogle Scholar
Sampedro, C., Bavle, H., Sanchez Lopez, J. L., Suarez Fernandez, R., Rodriguez Ramos, A., Molina, M. and Campoy Cervera, P., “A Flexible and Dynamic Mission Planning Architecture for UAV Swarm Coordination,” Proceedings of 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (ETSI_Informatica, 2016).CrossRefGoogle Scholar
Nascimento, T. P. and Saska, M., “Position and attitude control of multi-rotor aerial vehicles: A survey,” Ann. Rev. Control. 48, 129–146 (2019).CrossRefGoogle Scholar
Coste-Maniere, E. and Simmons, R., “Architecture, the Backbone of Robotic Systems,” Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), vol. 1 (IEEE, 2000) pp. 67–72.Google Scholar
Erdos, D., Erdos, A. and Watkins, S. E., “An experimental UAV system for search and rescue challenge,” IEEE Aerosp. Electron. Syst. Mag. 28(5), 32–37 (2013).CrossRefGoogle Scholar
Gu, J., Su, T., Wang, Q., Du, X. and Guizani, M., “Multiple moving targets surveillance based on a cooperative network for multi-UAV,” IEEE Commun. Mag. 56(4), 82–89 (2018).CrossRefGoogle Scholar
Schlotfeldt, B., Thakur, D., Atanasov, N., Kumar, V. and Pappas, G. J., “Anytime planning for decentralized multirobot active information gathering,” IEEE Robot. Autom. Lett. 3(2), 1025–1032 (2018).CrossRefGoogle Scholar
Aamodt, A. and Plaza, E., “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” AI Commun. 7(1), 39–59 (1994).CrossRefGoogle Scholar
Pinto, M. F., Melo, A. G., Marcato, A. L. and Urdiales, C., “Case-Based Reasoning Approach Applied to Surveillance System Using An Autonomous Unmanned Aerial Vehicle,” 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE) (IEEE, 2017) pp. 1324–1329.Google Scholar
Emel’yanov, S., Makarov, D., Panov, A. I and Yakovlev, K., “Multilayer cognitive architecture forAV control,” Cognit. Syst. Res. 39, 58–72 (2016).CrossRefGoogle Scholar
Stütz, P., Hummel, G., Kaiser, M., Schulte, A., Climent-Pérez, P., Remagnino, P., Lund, D., Magzoub, A., Tsado, Y., Gozdecki, J., Loziak, K., “UAV Integration Aspects within the Proactive Network,” World of UAV International Conference WoUCON 2015 (2015).Google Scholar
Hawkins, J. and Ahmad, S., “Why neurons have thousands of synapses, a theory of sequence memory in neocortex,” Front. Neural Circuits 10, 23 (2016).CrossRefGoogle ScholarPubMed
Cui, Y., Ahmad, S. and Hawkins, J., “Continuous online sequence learning with an unsupervised neural network model,” Neural Comput. 28(11), 2474–2504 (2016).CrossRefGoogle ScholarPubMed
Adams, N. E., “Bloom’s taxonomy of cognitive learning objectives,” J. Med. Lib. Assoc. JMLA 103(3), 152 (2015).CrossRefGoogle ScholarPubMed
Jiang, H. and Liang, Y., “Online path planning of autonomous UAVs for bearing-only standoff multi-target following in threat environment,” IEEE Access 6, 22 531–22 544 (2018).Google Scholar
Kwak, J. and Sung, Y., “Autonomous UAV flight control for GPS-based navigation,” IEEE Access 6, 37 947–37 955 (2018).CrossRefGoogle Scholar
Uhrmann, J. and Schulte, A., “Concept, design and evaluation of cognitive task-based UAV guidance,” J. Adv. Intell. Syst. 5(1), 145–158 (2012).Google Scholar
Barak, M. and Assal, M., “Robotics and stem learning: Students’ achievements in assignments according to the p3 task taxonomy-practice, problem solving, and projects,” Int. J. Tech. Des. Edu. 28(1), 121–144 (2018).CrossRefGoogle Scholar
Gageik, N., Benz, P. and Montenegro, S., “Obstacle detection and collision avoidance for a UAV with complementary low-cost sensors,” IEEE Access 3, 599–609 (2015).CrossRefGoogle Scholar
Das, T., “Intelligent techniques in decision making: A survey,” Indian J. Sci. Tech. 9(12), 1–6 (2016).CrossRefGoogle Scholar
Cummings, M., “Operator interaction with centralized versus decentralized UAV architectures,” In: Handbook of Unmanned Aerial Vehicles (Springer, 2015) pp. 977–992.CrossRefGoogle Scholar
Baccarelli, E., Naranjo, P. G. V., Scarpiniti, M., Shojafar, M. and Abawajy, J. H., “Fog of everything: Energy-efficient networked computing architectures, research challenges, and a case study,” IEEE Access 5, 9882–9910 (2017).CrossRefGoogle Scholar
Pinto, M. F., Marcato, A. L. M., Melo, A. G., Honório, L. M. and Urdiales, C., “A framework for analyzing fog-cloud computing cooperation applied to information processing of UAVs,” Wireless Commun. Mobile Comput. 2019, 1–14 (2019).Google Scholar
Zeng, F., Ji, Y. and Levine, M. D., “Contextual bag-of-words for robust visual tracking,” IEEE Trans. Image Process. 27(3), 1433–1447 (2018).CrossRefGoogle Scholar