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Flexible and Scalable Aggregation Behavior Method for Swarm Robots

Year 2020, Ejosat Special Issue 2020 (HORA), 100 - 109, 15.08.2020
https://doi.org/10.31590/ejosat.779162

Abstract

Swarm robotics has emerged as an approach that aims to perform a task with similar robots with limited and simple abilities, unlike a few robots with superior skills. This approach has been revealed by being inspired by living creatures such as ants, termites and bees living in flocks. In Swarm robotics, they must be gathered together so that robots can coordinate between them. For this reason, Aggregation is one of the basic behaviors applied in swarm robotics. In this study, aggregation behavior method is proposed for swarm robots. The proposed aggregation method combines only the limited distance and angle sensing capabilities of the swarm robots with the same characteristics, without a central controller between them. The proposed aggregation method consists of approach and a common directional motion controller, as well as collision and obstacle avoidance controllers. The approach controller allows the robots to move in the direction of the majority of the robots entering the detection area. Because robots have limited detection areas, they are aggregated only with robots they detect. In such a situation, robots form robot groups among themselves. A motion controller is used in a common direction for the robot groups that are formed to join. The motion controller in the common direction allows the grouped robots to move in the same direction, coordinated in a certain direction. With the joint movement of robot groups, robots unite when they meet. A separate controller has been developed for swarm robots to come close enough to hit each other and avoid obstacles and collisions if they encounter an obstacle. Each robot combines with the self organizing aggregation method only by deciding on its own. There is no need for a decentralized control unit. The movement of the robots with the method of aggregation constitutes the self organization organization. In the study, the proposed aggregation method was performed by systematic experiments by changing the arena size and the number of robots where the swarm robots exhibit their behavior in matlab simulation environment. According to the results obtained from systematic experiments, the performance of the proposed aggregation behavior was evaluated.

References

  • Abuelhaija, A., Jebrein, A., & Baldawi, T. (2020). Swarm robotics : Design and implementation. International Journal of Electrical and Computer Engineering, 10(2), 2173–2181. https://doi.org/10.11591/ijece.v10i2.pp2173-2181
  • Amé, J.-M., Halloy, J., Rivault, C., Detrain, C., & Deneubourg, J. L. (2006). Collegial decision making based on social amplification leads to optimal group formation. Proceedings of the National Academy of Sciences of the United States of America, 103(15), 5835–5840. https://doi.org/10.1073/pnas.0507877103
  • Arvin, F., Samsudin, K., Ramli, A. R., & Bekravi, M. (2011). Imitation of Honeybee Aggregation with Collective Behavior of Swarm Robots. International Journal of Computational Intelligence Systems, 4(4), 739–748. https://doi.org/10.1080/18756891.2011.9727825
  • Arvin, F., Turgut, A. E., Bazyari, F., Arikan, K. B., Bellotto, N., & Yue, S. (2014). Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method. Adaptive Behavior, 22(3), 189–206. https://doi.org/10.1177/1059712314528009
  • Bayındır, L. (2016). A review of swarm robotics tasks. Neurocomputing, 172, 292–321. https://doi.org/10.1016/J.NEUCOM.2015.05.116
  • Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1–41. https://doi.org/10.1007/s11721-012-0075-2
  • Camazine, S, Deneubourg, J. L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2003). Self-Organization in Biological Systems: (Princeton Studies in Complexity). Princeton University Press.
  • Camazine, Scott., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-organization in biological systems. Princeton University Press. Tarihinde adresinden erişildi https://press.princeton.edu/titles/7104.html
  • de Sá, A. O., Nedjah, N., & Mourelle, L. de M. (2017). Distributed and resilient localization algorithm for Swarm Robotic Systems. Applied Soft Computing, 57, 738–750. https://doi.org/10.1016/J.ASOC.2016.07.049
  • Dudek, G., Jenkin, M. M., Milios, E., & Wilkes, D. (1996). A taxonomy for multi-agent robotics. Autonomous Robots, 3(4), 375–397. https://doi.org/10.1007/BF00240651
  • Francesca, G., Brambilla, M., Trianni, V., Dorigo, M., & Birattari, M. (2012). Analysing an Evolved Robotic Behaviour Using a Biological Model of Collegial Decision Making (ss. 381–390). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33093-3_38
  • Gauci, M., Chen, J., Dodd, T. J., & Groß, R. (2014b). Evolving Aggregation Behaviors in Multi-Robot Systems with Binary Sensors (ss. 355–367). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55146-8_25
  • Gauci, M., Chen, J., Li, W., Dodd, T. J., & Groß, R. (2014a). Self-organized aggregation without computation. The International Journal of Robotics Research, 33(8), 1145–1161. https://doi.org/10.1177/0278364914525244
  • Gomes, J., Urbano, P., & Christensen, A. L. (2013). Evolution of swarm robotics systems with novelty search. Swarm Intelligence, 7(2–3), 115–144. https://doi.org/10.1007/s11721-013-0081-z
  • Hamann, H., Worn, H., Crailsheim, K., & Schmickl, T. (2008). Spatial macroscopic models of a bio-inspired robotic swarm algorithm. Içinde 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (ss. 1415–1420). IEEE. https://doi.org/10.1109/IROS.2008.4651038
  • Hu, D., Zhong, M., Zhang, X., & Yao, Y. (2014). Self-organized aggregation based on cockroach behavior in swarm robotics. Içinde Proceedings - 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2014 (C. 1, ss. 349–354). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/IHMSC.2014.92
  • Kernbach, S., Häbe, D., Kernbach, O., Thenius, R., Radspieler, G., Kimura, T., & Schmickl, T. (2013). Adaptive collective decision-making in limited robot swarms without communication. The International Journal of Robotics Research, 32(1), 35–55. https://doi.org/10.1177/0278364912468636
  • Khaldi, B., Harrou, F., Cherif, F., & Sun, Y. (2018a). Self-organization in aggregating robot swarms: A DW-KNN topological approach. Biosystems, 165, 106–121. https://doi.org/10.1016/J.BIOSYSTEMS.2018.01.005
  • Khaldi, B., Harrou, F., Cherif, F., & Sun, Y. (2019). Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation. IEEE Access, 7, 96372–96383. https://doi.org/10.1109/ACCESS.2019.2930677
  • Martínez-Clark, R., Cruz-Hernández, C., Pliego-Jimenez, J., & Arellano-Delgado, A. (2018). Control algorithms for the emergence of self-organized behaviours in swarms of differential-traction wheeled mobile robots. International Journal of Advanced Robotic Systems, 15(6), 172988141880643. https://doi.org/10.1177/1729881418806435
  • Nakano, R. C. S., Bandala, A., Faelden, G. E., Maningo, J. M., & Dadios, E. P. (2014). A genetic algorithm approach to swarm centroid tracking in quadrotor unmanned aerial vehicles. Içinde 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (ss. 1–6). IEEE. https://doi.org/10.1109/HNICEM.2014.7016217
  • Nedjah, N., & Junior, L. S. (2019). Review of methodologies and tasks in swarm robotics towards standardization. Swarm and Evolutionary Computation, 50, 100565. https://doi.org/10.1016/j.swevo.2019.100565
  • Şahin, E., & Bayındır, L. (2014). A Review of Studies in Swarm Robotics. Turkish Journal of Electrical Engineering and Computer Science, 15(2), 115–147. Tarihinde adresinden erişildi http://dergipark.gov.tr/tbtkelektrik/issue/12085/144468
  • Sathiya, V., & Chinnadurai, M. (2019). Evolutionary Algorithms-Based Multi-Objective Optimal Mobile Robot Trajectory Planning. Robotica, 1–20. https://doi.org/10.1017/S026357471800156X
  • Shao, J., Lin, H., & Zhang, K. (2013). Swarm robots reinforcement learning convergence Accuracy-based learning classifier systems with Gradient descent (XCS-GD). Içinde Proceedings of 2013 3rd International Conference on Computer Science and Network Technology (ss. 1306–1309). IEEE. https://doi.org/10.1109/ICCSNT.2013.6967341
  • Shlyakhov, N. E., Vatamaniuk, I. V, & Ronzhin, A. L. (2017). Survey of Methods and Algorithms of Robot Swarm Aggregation. Journal of Physics: Conference Series, 803, 012146. https://doi.org/10.1088/1742-6596/803/1/012146
  • Soysal, O., & Sahin, E. (2005). Probabilistic aggregation strategies in swarm robotic systems. Içinde Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005. (ss. 325–332). IEEE. https://doi.org/10.1109/SIS.2005.1501639
  • Soysal, Onur, Bahçeci, E., & Şahin Erol. (2007). Aggregation in Swarm Robotic Systems: Evolution and Probabilistic Control. Turkish Journal of Electrical Engineering and Computer Science, 15(2), 199–225. http://dergipark.gov.tr/download/article-file/125895
  • Trianni, V., Groß, R., Labella, T. H., Şahin, E., & Dorigo, M. (2010). Evolving Aggregation Behaviors in a Swarm of Robots (ss. 865–874). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39432-7_93
  • Vanualailai, J., & Sharma, B. N. (2010). A Lagrangian-based Swarming Behavior in the Absence of Obstacles. http://repository.usp.ac.fj/7380/
  • Yan, X., Liang, A., & Guan, H. (2011). An algorithm for self-organized aggregation of swarm robotics using timer. Içinde 2011 IEEE Symposium on Swarm Intelligence (ss. 1–7). IEEE. https://doi.org/10.1109/SIS.2011.5952567
  • Yılmaz, Z., & Bayındır, L. (2019). Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo. European Journal of Science and Technology, 513–529. https://doi.org/10.31590/ejosat.642840

Sürü Robotları için Esnek ve Ölçeklenebilir Toplanma Davranışı Metodu

Year 2020, Ejosat Special Issue 2020 (HORA), 100 - 109, 15.08.2020
https://doi.org/10.31590/ejosat.779162

Abstract

Sürü robotiği, bir görevi üstün yeteneklere sahip birkaç robotun gerçekleştirmesinden farklı olarak, sınırlı ve basit yeteneklere sahip benzer robotlarla gerçekleştirmeyi amaçlayan bir yaklaşım olarak ortaya çıkmıştır. Bu yaklaşım doğada sürü halinde yaşayan karıncalar, termitler ve arılar gibi canlılardan ilham alınarak ortaya koyulmuştur. Sürü robotiğinde robotların aralarında koordinasyon kurabilmeleri için bir araya toplanmalıdırlar. Bu sebepten toplanma sürü robotiğinde uygulanan temel davranışlardan biridir. Bu çalışmada sürü robotları için toplanma davranışı metodu önerilmiştir. Önerilen toplanma metodu aynı özelliklere sahip sürü robotlarının, aralarında merkezi bir kontrolcü olmadan sadece sınırlı mesafe ve açı algılama sensör verileri ile bir araya toplanmaktadırlar. Önerilen toplanma metodu yaklaşma ve ortak yönde hareket kontrolcüsü ayrıca çarpışmadan ve engelden kaçınma kontrolcülerinden oluşmaktadır. Yaklaşma kontrolcüsü sürü robotlarını algılama alanı içerine giren robotlardan çoğunluğun olduğu yöne ilerlemelerini sağlamaktadır. Robotlar sınırlı algılama alanına sahip olduğundan sadece algıladıkları robotlarla toplanırlar. Böyle bir durumda robotlar kendi aralarında robot grupları oluştururlar. Oluşan robot gruplarının birleşebilmeleri için ortak yönde hareket kontrolcüsü kullanılmaktadır. Ortak yönde hareket kontrolcüsü gruplaşan robotları belirli bir yönde koordineli bir şekilde aynı yönde hareketini sağlar. Robot gruplarının ortak hareketi ile robotlar karşılaştıklarında birleşirler. Sürü robotlarının birbirlerine çarpacak kadar yaklaşmaları ve bir engelle karşılaşması durumunda engelden ve çarpışmadan kaçınmaları için ayrı bir kontrolör geliştirilmiştir. Her bir robot toplanma metodu ile sadece kendi kendine karar vererek bir araya gelmektedir. Bir merkezi kontrol birimine ihtiyaç yoktur. Robotların toplanma metodu ile hareketi öz örgütlenme (self organizing) organizasyonunu oluşturmaktadır. Çalışmada matlab benzetim ortamında sürü robotlarının toplanma davranışını sergiledikleri arena boyutunu ve robot sayısını değiştirerek önerilen toplanma metodu sistematik deneyler yoluyla incelenmiştir. Sistematik deneylerden elde edilen sonuçlara göre önerilen toplanma davranışının performansı değerlendirilmiştir.

References

  • Abuelhaija, A., Jebrein, A., & Baldawi, T. (2020). Swarm robotics : Design and implementation. International Journal of Electrical and Computer Engineering, 10(2), 2173–2181. https://doi.org/10.11591/ijece.v10i2.pp2173-2181
  • Amé, J.-M., Halloy, J., Rivault, C., Detrain, C., & Deneubourg, J. L. (2006). Collegial decision making based on social amplification leads to optimal group formation. Proceedings of the National Academy of Sciences of the United States of America, 103(15), 5835–5840. https://doi.org/10.1073/pnas.0507877103
  • Arvin, F., Samsudin, K., Ramli, A. R., & Bekravi, M. (2011). Imitation of Honeybee Aggregation with Collective Behavior of Swarm Robots. International Journal of Computational Intelligence Systems, 4(4), 739–748. https://doi.org/10.1080/18756891.2011.9727825
  • Arvin, F., Turgut, A. E., Bazyari, F., Arikan, K. B., Bellotto, N., & Yue, S. (2014). Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method. Adaptive Behavior, 22(3), 189–206. https://doi.org/10.1177/1059712314528009
  • Bayındır, L. (2016). A review of swarm robotics tasks. Neurocomputing, 172, 292–321. https://doi.org/10.1016/J.NEUCOM.2015.05.116
  • Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1–41. https://doi.org/10.1007/s11721-012-0075-2
  • Camazine, S, Deneubourg, J. L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2003). Self-Organization in Biological Systems: (Princeton Studies in Complexity). Princeton University Press.
  • Camazine, Scott., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-organization in biological systems. Princeton University Press. Tarihinde adresinden erişildi https://press.princeton.edu/titles/7104.html
  • de Sá, A. O., Nedjah, N., & Mourelle, L. de M. (2017). Distributed and resilient localization algorithm for Swarm Robotic Systems. Applied Soft Computing, 57, 738–750. https://doi.org/10.1016/J.ASOC.2016.07.049
  • Dudek, G., Jenkin, M. M., Milios, E., & Wilkes, D. (1996). A taxonomy for multi-agent robotics. Autonomous Robots, 3(4), 375–397. https://doi.org/10.1007/BF00240651
  • Francesca, G., Brambilla, M., Trianni, V., Dorigo, M., & Birattari, M. (2012). Analysing an Evolved Robotic Behaviour Using a Biological Model of Collegial Decision Making (ss. 381–390). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33093-3_38
  • Gauci, M., Chen, J., Dodd, T. J., & Groß, R. (2014b). Evolving Aggregation Behaviors in Multi-Robot Systems with Binary Sensors (ss. 355–367). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55146-8_25
  • Gauci, M., Chen, J., Li, W., Dodd, T. J., & Groß, R. (2014a). Self-organized aggregation without computation. The International Journal of Robotics Research, 33(8), 1145–1161. https://doi.org/10.1177/0278364914525244
  • Gomes, J., Urbano, P., & Christensen, A. L. (2013). Evolution of swarm robotics systems with novelty search. Swarm Intelligence, 7(2–3), 115–144. https://doi.org/10.1007/s11721-013-0081-z
  • Hamann, H., Worn, H., Crailsheim, K., & Schmickl, T. (2008). Spatial macroscopic models of a bio-inspired robotic swarm algorithm. Içinde 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (ss. 1415–1420). IEEE. https://doi.org/10.1109/IROS.2008.4651038
  • Hu, D., Zhong, M., Zhang, X., & Yao, Y. (2014). Self-organized aggregation based on cockroach behavior in swarm robotics. Içinde Proceedings - 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2014 (C. 1, ss. 349–354). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/IHMSC.2014.92
  • Kernbach, S., Häbe, D., Kernbach, O., Thenius, R., Radspieler, G., Kimura, T., & Schmickl, T. (2013). Adaptive collective decision-making in limited robot swarms without communication. The International Journal of Robotics Research, 32(1), 35–55. https://doi.org/10.1177/0278364912468636
  • Khaldi, B., Harrou, F., Cherif, F., & Sun, Y. (2018a). Self-organization in aggregating robot swarms: A DW-KNN topological approach. Biosystems, 165, 106–121. https://doi.org/10.1016/J.BIOSYSTEMS.2018.01.005
  • Khaldi, B., Harrou, F., Cherif, F., & Sun, Y. (2019). Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation. IEEE Access, 7, 96372–96383. https://doi.org/10.1109/ACCESS.2019.2930677
  • Martínez-Clark, R., Cruz-Hernández, C., Pliego-Jimenez, J., & Arellano-Delgado, A. (2018). Control algorithms for the emergence of self-organized behaviours in swarms of differential-traction wheeled mobile robots. International Journal of Advanced Robotic Systems, 15(6), 172988141880643. https://doi.org/10.1177/1729881418806435
  • Nakano, R. C. S., Bandala, A., Faelden, G. E., Maningo, J. M., & Dadios, E. P. (2014). A genetic algorithm approach to swarm centroid tracking in quadrotor unmanned aerial vehicles. Içinde 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (ss. 1–6). IEEE. https://doi.org/10.1109/HNICEM.2014.7016217
  • Nedjah, N., & Junior, L. S. (2019). Review of methodologies and tasks in swarm robotics towards standardization. Swarm and Evolutionary Computation, 50, 100565. https://doi.org/10.1016/j.swevo.2019.100565
  • Şahin, E., & Bayındır, L. (2014). A Review of Studies in Swarm Robotics. Turkish Journal of Electrical Engineering and Computer Science, 15(2), 115–147. Tarihinde adresinden erişildi http://dergipark.gov.tr/tbtkelektrik/issue/12085/144468
  • Sathiya, V., & Chinnadurai, M. (2019). Evolutionary Algorithms-Based Multi-Objective Optimal Mobile Robot Trajectory Planning. Robotica, 1–20. https://doi.org/10.1017/S026357471800156X
  • Shao, J., Lin, H., & Zhang, K. (2013). Swarm robots reinforcement learning convergence Accuracy-based learning classifier systems with Gradient descent (XCS-GD). Içinde Proceedings of 2013 3rd International Conference on Computer Science and Network Technology (ss. 1306–1309). IEEE. https://doi.org/10.1109/ICCSNT.2013.6967341
  • Shlyakhov, N. E., Vatamaniuk, I. V, & Ronzhin, A. L. (2017). Survey of Methods and Algorithms of Robot Swarm Aggregation. Journal of Physics: Conference Series, 803, 012146. https://doi.org/10.1088/1742-6596/803/1/012146
  • Soysal, O., & Sahin, E. (2005). Probabilistic aggregation strategies in swarm robotic systems. Içinde Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005. (ss. 325–332). IEEE. https://doi.org/10.1109/SIS.2005.1501639
  • Soysal, Onur, Bahçeci, E., & Şahin Erol. (2007). Aggregation in Swarm Robotic Systems: Evolution and Probabilistic Control. Turkish Journal of Electrical Engineering and Computer Science, 15(2), 199–225. http://dergipark.gov.tr/download/article-file/125895
  • Trianni, V., Groß, R., Labella, T. H., Şahin, E., & Dorigo, M. (2010). Evolving Aggregation Behaviors in a Swarm of Robots (ss. 865–874). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39432-7_93
  • Vanualailai, J., & Sharma, B. N. (2010). A Lagrangian-based Swarming Behavior in the Absence of Obstacles. http://repository.usp.ac.fj/7380/
  • Yan, X., Liang, A., & Guan, H. (2011). An algorithm for self-organized aggregation of swarm robotics using timer. Içinde 2011 IEEE Symposium on Swarm Intelligence (ss. 1–7). IEEE. https://doi.org/10.1109/SIS.2011.5952567
  • Yılmaz, Z., & Bayındır, L. (2019). Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo. European Journal of Science and Technology, 513–529. https://doi.org/10.31590/ejosat.642840
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Oğuz Mısır 0000-0002-3785-1795

Levent Gökrem This is me

Publication Date August 15, 2020
Published in Issue Year 2020 Ejosat Special Issue 2020 (HORA)

Cite

APA Mısır, O., & Gökrem, L. (2020). Sürü Robotları için Esnek ve Ölçeklenebilir Toplanma Davranışı Metodu. Avrupa Bilim Ve Teknoloji Dergisi100-109. https://doi.org/10.31590/ejosat.779162