Abstract
Robotic group collaboration in a densely cluttered terrain is one of the main problems in mobile robotics control. The chapter describes the basic set of tasks solved in model of robotic group behavior during the distributed search of an object (goal) with the parallel mapping. Navigation scheme uses the benefits of authors original technical vision system (TVS) based on dynamic triangulation principles. According to the TVS, output data were implemented fuzzy logic rules of resolution stabilization for improving the data exchange. Modified dynamic communication network model and implemented propagation of information with a feedback method for data exchange inside the robotic group. For forming the continuous and energy saving trajectory, authors are proposing to use two-steps post processing method of path planning with polygon approximation. Combination of our collective TVS scans fusion and modified dynamic data exchange network forming method with dovetailing of the known path planning methods can improve the robotic motion planning and navigation in unknown cluttered terrain.
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Atyabi, A., Phon-Amnuaisuk, S., & Ho, C. K. (2010). Navigating a robotic swarm in an uncharted 2d landscape. Applied Soft Computing, 10(1), 149–169.
Levi, P., Meister, E., & Schlachter, F. (2014). Reconfigurable swarm robots produce self-assembling and self-repairing organisms. Robotics and Autonomous Systems, 62(10), 1371–1376.
de Sá, A. O., Nedjah, N., & de Macedo Mourelle, L. (2017). Distributed and resilient localization algorithm for swarm robotic systems. Applied Soft Computing, 57, 738–750.
Teoh, E. R., & Kidd, D. G. (2017). Rage against the machine? Google’s self-driving cars versus human drivers. Journal of Safety Research, 63, 57–60.
Morales, Y., Watanabe, A., Ferreri, F., Even, J., Shinozawa, K., & Hagita, N. (2018). Passenger discomfort map for autonomous navigation in a robotic wheelchair. Robotics and Autonomous Systems, 103, 13–26.
Bond, A. H., & Gasser, L. (1992). A subject-indexed bibliography of distributed artificial intelligence. IEEE Transactions on Systems, Man, and Cybernetics, 22(6), 1260–1281.
Bond, A. H., & Gasser, L. (2014). Readings in distributed artificial intelligence. San Mateo, CA: Morgan Kaufmann.
Boes, J., & Migeon, F. (2017). Self-organizing multi-agent systems for the control of complex systems. Journal of Systems and Software, 134, 12–28.
Tan, Y., & Zheng, Z.-y. (2013). Research advance in swarm robotics. Defence Technology, 9(1), 18–39.
Nebti, S., & Boukerram, A. (2017). Swarm intelligence inspired classifiers for facial recognition. Swarm and Evolutionary Computation, 32, 150–166.
Mavrovouniotis, M., Li, C., & Yang, S. (2017). A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, 33, 1–17.
Parr, L. A., Winslow, J. T., Hopkins, W. D., & de Waal, F. (2000). Recognizing facial cues: Individual discrimination by chimpanzees (pan troglodytes) and rhesus monkeys (Macaca mulatta). Journal of Comparative Psychology, 114(1), 47.
Parr, L. A., & de Waal, F. B. (1999). Visual kin recognition in chimpanzees. Nature, 399(6737), 647.
Shapiro, J. A. (1998). Thinking about bacterial populations as multicellular organisms. Annual Reviews in Microbiology, 52(1), 81–104.
Costerton, J. W., Lewandowski, Z., Caldwell, D. E., Korber, D. R., & Lappin-Scott, H. M. (1995). Microbial biofilms. Annual Reviews in Microbiology,49(1), 711–745.
Wallraff, H. G., & Wallraff, H. G. (2005). Avian navigation: Pigeon homing as a paradigm. New York: Springer.
Jackson, D. E., & Ratnieks, F. L. (2006). Communication in ants.’ Current Biology, 16(15), R570–R574.
Goss, S., Aron, S., Deneubourg, J.-L., & Pasteels, J. M. (1989). Self-organized shortcuts in the argentine ant. Naturwissenschaften, 76(12), 579–581.
Ravary, F., Lecoutey, E., Kaminski, G., Châline, N., & Jaisson, P. (2007). Individual experience alone can generate lasting division of labor in ants. Current Biology, 17(15), 1308–1312.
Buhl, J., Sumpter, D. J., Couzin, I. D., Hale, J. J., Despland, E., Miller, E. R., et al. (2006). From disorder to order in marching locusts. Science, 312(5778), 1402–1406.
Bone, Q., & Moore, R. (2008). Biology of fishes. New York: Taylor & Francis.
Pitcher, T., Magurran, A., & Winfield, I. (1982). Fish in larger shoals find food faster. Behavioral Ecology and Sociobiology, 10(2), 149–151.
Moyle, P. B., & Cech, J. J. (2004). Fishes: an introduction to ichthyology. No. 597. Upper Saddle River, NJ: Pearson Prentice Hall.
Dyer, J. R., Ioannou, C. C., Morrell, L. J., Croft, D. P., Couzin, I. D., Waters, D. A., et al. (2008). Consensus decision making in human crowds. Animal Behaviour, 75(2), 461–470.
Marocco, D., & Nolfi, S. (2006). Origins of communication in evolving robots. In International Conference on Simulation of Adaptive Behavior (pp. 789–803). Heidelberg: Springer.
Hayes, A. T., Martinoli, A., & Goodman, R. M. (2003). Swarm robotic odor localization: Off-line optimization and validation with real robots. Robotica, 21(4), 427–441.
de Oca, M. A. M., Ferrante, E., Scheidler, A., Pinciroli, C., Birattari, M., & Dorigo, M. (2011). Majority-rule opinion dynamics with differential latency: A mechanism for self-organized collective decision-making. Swarm Intelligence, 5(3–4), 305–327.
Scheidler, A., Brutschy, A., Ferrante, E., & Dorigo, M. (2016). The k-unanimity rule for self-organized decision-making in swarms of robots. IEEE Transactions on Cybernetics, 46(5), 1175–1188.
Valentini, G., Hamann, H., & Dorigo, M. (2015). Efficient decision-making in a self-organizing robot swarm: On the speed versus accuracy trade-off. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’15, (Richland, SC) (pp. 1305–1314). International Foundation for Autonomous Agents and Multiagent Systems.
Wawerla, J., Sukhatme, G. S., & Mataric, M. J. (2002). Collective construction with multiple robots. In 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (Vol. 3, pp. 2696–2701). Piscataway: IEEE.
Werfel, J., Bar-Yam, Y., & Nagpal, R. (2005). Building patterned structures with robot swarms. In Proceedings of the IJCAI (pp. 1495–1504).
Allwright, M., Bhalla, N., El-faham, H., Antoun, A., Pinciroli, C., & Dorigo, M. (2014). Srocs: Leveraging stigmergy on a multi-robot construction platform for unknown environments. In International Conference on Swarm Intelligence (pp. 158–169). Berlin: Springer.
Groß, R., Bonani, M., Mondada, F., & Dorigo, M. (2006). Autonomous self-assembly in a swarm-bot. In Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE 2005) (pp. 314–322). Berlin: Springer.
Tuci, E., Ampatzis, C., Trianni, V., Christensen, A. L., & Dorigo, M. (2008). Self-assembly in physical autonomous robots-the evolutionary robotics approach. In Proceedings of the ALIFE (pp. 616–623).
Trianni, V., Nolfi, S., & Dorigo, M. Cooperative hole avoidance in a swarm-bot. Robotics and Autonomous Systems, 54(2), 97–103 (2006)
O’Grady, R., Groß, R., Christensen, A. L., Dorigo, M. (2010). Self-assembly strategies in a group of autonomous mobile robots,” Autonomous Robots, 28(4), 439–455.
Bashyal, S., & Venayagamoorthy, G. K. (2008, Sept) Human swarm interaction for radiation source search and localization. In 2008 IEEE Swarm Intelligence Symposium (pp. 1–8).
Walker, P., Amraii, S. A., Chakraborty, N., Lewis, M., & Sycara, K. (Sept 2014). Human control of robot swarms with dynamic leaders. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1108–1113).
Kolling, A., Sycara, K., Nunnally, S., & Lewis, M. (June 2013). Human-swarm interaction: An experimental study of two types of interaction with foraging swarms. Journal of Human-Robot Interaction, 2, 103–129.
O’Grady, R., Christensen, A. L., & Dorigo, M. (2009). Swarmorph: Multirobot morphogenesis using directional self-assembly. IEEE Transactions on Robotics, 25(3), 738–743.
Brambilla, M., Pinciroli, C., Birattari, M., & Dorigo, M. (2009). A reliable distributed algorithm for group size estimation with minimal communication requirements. In International Conference on Advanced Robotics, 2009. ICAR 2009. (pp. 1–6). Piscataway: IEEE.
Bayındır, L. (2016). A review of swarm robotics tasks. Neurocomputing,172, 292–321.
Chen, S., & Fang, H. (2005). Modeling and behavior analysis of large-scale social foraging swarm. Control and Decision, 20(12), 1392.
Beni, G. (1988). The concept of cellular robotic system. In IEEE International Symposium on Intelligent Control, 1988. Proceedings (pp. 57–62). Piscataway: IEEE.
Asama, H., Matsumoto, A., & Ishida, Y. (1989). Design of an autonomous and distributed robot system: Actress. In Proceedings of the IROS (vol. 89, pp. 283–290).
Payton, D., Daily, M., Estowski, R., Howard, M., & Lee, C. (2001). Pheromone robotics. Autonomous Robots, 11(3), 319–324.
Payton, D., Estkowski, R., & Howard, M. (2003). Compound behaviors in pheromone robotics. Robotics and Autonomous Systems, 44(3–4), 229–240.
Şahin, E. (2004). Swarm robotics: From sources of inspiration to domains of application. In International Workshop on Swarm Robotics (pp. 10–20). Heidelberg: Springer.
McLurkin, J., & Smith, J. (2004). Distributed algorithms for dispersion in indoor environments using a swarm of autonomous mobile robots. In 7th International Symposium on Distributed Autonomous Robotic Systems (DARS), Citeseer.
Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., et al. (2009). The e-puck, a robot designed for education in engineering. In Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions (Vol. 1, pp. 59–65). IPCB: Instituto Politécnico de Castelo Branco.
Turgut, A. E., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008). Self-organized flocking in mobile robot swarms. Swarm Intelligence, 2(2–4), 97–120.
Rubenstein, M., Ahler, C., & Nagpal, R. (2012). Kilobot: A low cost scalable robot system for collective behaviors. In 2012 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3293–3298). Piscataway: IEEE.
Seyfried, J., Szymanski, M., Bender, N., Estaña, R., Thiel, M., & Wörn, H. (2004). The i-swarm project: Intelligent small world autonomous robots for micro-manipulation. In International Workshop on Swarm Robotics (pp. 70–83). Berlin: Springer.
Beshers, S. N., & Fewell, J. H. (2001). Models of division of labor in social Insects. Annual Review of Entomology, 46(1), 413–440.
Trianni, V., Tuci, E., Ampatzis, C., & Dorigo, M. (2014). Evolutionary swarm robotics: A theoretical and methodological itinerary from individual neuro-controllers to collective behaviours. The Horizons of Evolutionary Robotics (pp. 153–160). New York: ACM .
Konolige, K., Fox, D., Ortiz, C., Agno, A., Eriksen, M., Limketkai, B., et al. (2008). Centibots: Very large scale distributed robotic teams. In Experimental Robotics IX (pp. 131–140). Springer.
Dorigo, M., Floreano, D., Gambardella, L. M., Mondada, F., Nolfi, S., Baaboura, T., et al. (2013). Swarmanoid: A novel concept for the study of heterogeneous robotic swarms. IEEE Robotics & Automation Magazine, 20(4), 60–71.
Rybski, P. E., Burt, I., Dahlin, T., Gini, M., Hougen, D. F., Krantz, D. G., et al. (2001). System architecture for versatile autonomous and teleoperated control of multiple miniature robots. In Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation, 2001 (Vol. 3, pp. 2917-2922). Piscataway: IEEE.
Suárez, P., Iglesias, A., & Gálvez, A. (2018). Make robots be bats: Specializing robotic swarms to the bat algorithm. Swarm and Evolutionary Computation, 44, 113–129.
Vilão, C. O., Perico, D. H., Silva, I. J., Homem, T. P., Tonidandel, F., & Bianchi, R. A. (2014). A single camera vision system for a humanoid robot. In 2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol (SBR LARS Robocontrol) (pp. 181–186). Piscataway: IEEE.
Gryaznov, N., & Lopota, A. (2015). Computer vision for mobile on-ground robotics Procedia Engineering, 100, 1376–1380.
Scaramuzza, D., Achtelik, M. C., Doitsidis, L., Friedrich, F., Kosmatopoulos, E., Martinelli, A., et al. (2014). Vision-controlled micro flying robots: From system design to autonomous navigation and mapping in GPS-denied environments. IEEE Robotics and Automation Magazine, 21(3), 26–40.
Alenyà, G., Foix, S., & Torras, C. (2014). ToF cameras for active vision in robotics. Sensors and Actuators A: Physical, 218, 10–22.
Fan, Q., Sun, B., Sun, Y. Wu, Y., & Zhuang, X. (2017). Data fusion for indoor mobile robot positioning based on tightly coupled INS/UWB. The Journal of Navigation, 70(5), 1079–1097.
Sabo, C., Chisholm, R., Petterson, A., & Cope, A. (2017). A lightweight, inexpensive robotic system for insect vision. Arthropod Structure and Development, 46(5), 689–702.
Wahrmann, D., Hildebrandt, A.-C., Wittmann, R., Sygulla, F., Rixen, D., & Buschmann, T. (2016). Fast object approximation for real-time 3d obstacle avoidance with biped robots. In 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 38–45). Piscataway: IEEE.
McGuire, K., de Croon, G., De Wagter, C., Tuyls, K., & Kappen, H. J. (2017). Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone. IEEE Robotics and Automation Letters, 2(2), 1070–1076.
Li, J.-H., Ho, Y.-S., & Huang, J.-J. (2018). Line tracking with pixy cameras on a wheeled robot prototype. In 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) (pp. 1–2). Piscataway: IEEE.
Huang, A. S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D., et al. (2017). Visual odometry and mapping for autonomous flight using an RGB-D camera. In Robotics Research (pp. 235–252). Cham: Springer.
Starr, J. W., & Lattimer, B. (2017). Evidential sensor fusion of long-wavelength infrared stereo vision and 3D-lidar for rangefinding in fire environments. Fire Technology, 53(6), 1961–1983.
Yoo, H. W., Druml, N., Brunner, D., Schwarzl, C., Thurner, T., Hennecke, M., et al. (2018). MEMS-based lidar for autonomous driving. E & I Elektrotechnik und Informationstechnik (pp. 1–8).
Zhang, J., & Singh, S. (2017). Low-drift and real-time lidar odometry and mapping. Autonomous Robots, 41(2), 401–416.
Kinnell, P., Rymer, T., Hodgson, J., Justham, L., & Jackson, M. (2017). Autonomous metrology for robot mounted 3D vision systems. CIRP Annals, 66(1), 483–486.
Šuligoj, F., Šekoranja, B., Švaco, M., & Jerbić, B. (2014). Object tracking with a multiagent robot system and a stereo vision camera. Procedia Engineering, 69, 968–973.
Ferreira, M., Costa, P., Rocha, L., & Moreira, A. P. (2016). Stereo-based real-time 6-D of work tool tracking for robot programing by demonstration. The International Journal of Advanced Manufacturing Technology, 85(1–4), 57–69.
Pellegrini, S., & Iocchi, L. (2007, Dec) Human posture tracking and classification through stereo vision and 3D model matching. EURASIP Journal on Image and Video Processing, 2008, 476151.
Radhakrishnamurthy, H. C., Murugesapandian, P., Ramachandran, N., & Yaacob, S. (2017). Stereo vision system for a bin picking adept robot. Malaysian Journal of Computer Science, 20(1), 91–98.
Sergiyenko, O. Y. (2010). Optoelectronic system for mobile robot navigation. Optoelectronics, Instrumentation and Data Processing, 46(5), 414–428.
Rodriguez-Quinonez, J. C., Sergiyenko, O., Gonzalez-Navarro, F. F., Basaca-Preciado, L., & Tyrsa, V. (2013). Surface recognition improvement in 3D medical laser scanner using Levenberg–Marquardt method. Signal Processing, 93(2), 378–386.
Basaca-Preciado, L. C., Sergiyenko, O. Y., Rodríguez-Quinonez, J. C., Garcia, X., Tyrsa, V. V., Rivas-Lopez, M., et al. (2014). Optical 3D laser measurement system for navigation of autonomous mobile robot. Optics and Lasers in Engineering, 54, 159–169.
Sergiyenko, O. Y., Ivanov, M. V., Tyrsa, V., Kartashov, V. M., Rivas-López, M., Hernández-Balbuena, D., et al. (2016). Data transferring model determination in robotic group. Robotics and Autonomous Systems, 83, 251–260.
Lindner, L., Sergiyenko, O., Rivas-López, M., Valdez-Salas, B., Rodríguez-Quiñonez, J. C., Hernández-Balbuena, D., et al. (2016). Machine vision system for UAV navigation. In International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC) (pp. 1–6). Piscataway: IEEE.
Lindner, L., Sergiyenko, O., Rivas-López, M., Hernández-Balbuena, D., Flores-Fuentes, W., Rodríguez-Quiñonez, J. C., et al. (2017). Exact laser beam positioning for measurement of vegetation vitality. Industrial Robot: An International Journal, 44(4), 532–541.
Lindner, L., Sergiyenko, O., Rodríguez-Quiñonez, J. C., Tyrsa, V., Mercorelli, P., Fuentes, W. F., et al. (2015). Continuous 3D scanning mode using servomotors instead of stepping motors in dynamic laser triangulation. In 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE) (pp. 944–949). Piscataway: IEEE.
Lindner, L., Sergiyenko, O., Rodríguez-Quiñonez, J. C., Rivas-Lopez, M., Hernandez-Balbuena, D., Flores-Fuentes, W., et al. (2016). Mobile robot vision system using continuous laser scanning for industrial application. Industrial Robot: An International Journal, 43(4), 360–369.
Sergiyenko, O., Hernandez, W., Tyrsa, V., Cruz, L. F. D., Starostenko, O., & Peña-Cabrera, M. (2009). Remote sensor for spatial measurements by using optical scanning. Sensors, 9(7), 5477–5492.
Básaca, L. C., Rodríguez, J., Sergiyenko, O. Y., Tyrsa, V. V., Hernández, W., Hipólito, J. I. N., et al. (2010). Resolution improvement of dynamic triangulation method for 3D vision system in robot navigation task. In IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society (pp. 2886–2891). Piscataway: IEEE.
Ivanov, M., Lindner, L., Sergiyenko, O., Rodríguez-Quiñonez, J. C., Flores-Fuentes, W., & Rivas-Lopez, M. (2019). Mobile robot path planning using continuous laser scanning. In Optoelectronics in Machine Vision-Based Theories and Applications (pp. 338–372). Hershey: IGI Global.
Garcia-Cruz, X., Sergiyenko, O. Y., Tyrsa, V., Rivas-Lopez, M., Hernandez-Balbuena, D., Rodriguez-Quiñonez, J., et al. (2014). Optimization of 3D laser scanning speed by use of combined variable step. Optics and Lasers in Engineering, 54, 141–151.
Vincent, R., Morisset, B., Agno, A., Eriksen, M., & Ortiz, C. (2008). Centibots: Large-scale autonomous robotic search and rescue experiment. In 2nd International Joint Topical Meeting on Emergency Preparedness & Response and Robotics & Remote Systems.
Grymin, D. J., Neas, C. B., & Farhood, M. (2014). A hierarchical approach for primitive-based motion planning and control of autonomous vehicles. Robotics and Autonomous Systems, 62(2), 214–228.
Kovács, B., Szayer, G., Tajti, F., Burdelis, M., & Korondi, P. (2016). A novel potential field method for path planning of mobile robots by adapting animal motion attributes. Robotics and Autonomous Systems, 82, 24–34.
Ali, A. A., Rashid, A. T., Frasca, M., & Fortuna, L. (2016). An algorithm for multi-robot collision-free navigation based on shortest distance. Robotics and Autonomous Systems, 75, 119–128.
Duchoň, F., Babinec, A., Kajan, M., Beňo, P., Florek, M., Fico, T., et al. (2014). Path planning with modified a star algorithm for a mobile robot. Procedia Engineering, 96, 59–69.
Kawabata, K., Ma, L., Xue, J., Zhu, C., & Zheng, N. (2015). A path generation for automated vehicle based on Bézier curve and via-points. Robotics and Autonomous Systems, 74, 243–252.
Han, L., Yashiro, H., Nejad, H. T. N., Do, Q. H., & Mita, S. (2010, June). Bézier curve based path planning for autonomous vehicle in urban environment. In 2010 IEEE Intelligent Vehicles Symposium (pp. 1036–1042).
Lugo-Cárdenas, I., Flores, G., Salazar, S., & Lozano, R. (2014). Dubins path generation for a fixed wing UAV. In 2014 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 339–346). Piscataway: IEEE.
Karapetyan, N., Moulton, J., Lewis, J. S., Li, A. Q., O’Kane, J. M., & Rekleitis, I. (2018). Multi-robot Dubins coverage with autonomous surface vehicles. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2373–2379). Piscataway: IEEE.
Jha, B., Turetsky, V., & Shima, T. (2018). Robust path tracking by a Dubins ground vehicle. IEEE Transactions on Control Systems Technology, 99, 1–8.
Wang, Z., Liu, L., Long, T., Yu, C., & Kou, J. (2014). Enhanced sparse a* search for UAV path planning using Dubins path estimation. In 2014 33rd Chinese Control Conference (CCC) (pp. 738–742). Piscataway: IEEE.
Braem, B., Latre, B., Moerman, I., Blondia, C., & Demeester, P. (2006). The wireless autonomous spanning tree protocol for multihop wireless body area networks. In 2006 Third Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services (pp. 1–8). Piscataway: IEEE.
Fedyk, D., Ashwood-Smith, P., Allan, D., Bragg, A., & Unbehagen, P. (2012). IS-IS extensions supporting IEEE 802.1aq shortest path bridging. Technical Report.
Nguyen, H. T., & Walker, E. A. (2005). A first course in fuzzy logic. Boca Raton: CRC Press.
Duarte, M., Silva, F., Rodrigues, T., Oliveira, S. M., & Christensen, A. L. (2014). Jbotevolver: A versatile simulation platform for evolutionary robotics. In Proceedings of the 14th International Conference on the Synthesis & Simulation of Living Systems. MIT Press, Cambridge, MA (pp. 210–211). Citeseer.
Browning, B., & Tryzelaar, E. (2003). Übersim: A multi-robot simulator for robot soccer. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 948–949). New York, ACM.
Zhibao, S., Haojie, Z., & Sen, Z. (2017). A robotic simulation system combined USARSIM and RCS library. In 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) (pp. 240–243). New York, IEEE.
Klein, J., & Spector, L. (2009). 3D multi-agent simulations in the breve simulation environment. In Artificial Life Models in Software (pp. 79–106). New York: Springer.
Rohmer, E., Singh, S. P., & Freese, M. (2013). V-rep: A versatile and scalable robot simulation framework. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1321–1326). Piscataway: IEEE.
Michel, O. (2004). Cyberbotics ltd. webots: Professional mobile robot simulation. International Journal of Advanced Robotic Systems, 1(1), 5.
Furrer, F., Burri, M., Achtelik, M., & Siegwart, R. (2016). Rotors’ a modular gazebo MAV simulator framework. In Robot operating system (ROS) (pp. 595–625). Berlin: Springer.
Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., et al. (2011). Argos: A modular, multi-engine simulator for heterogeneous swarm robotics. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5027–5034). Piscataway: IEEE.
Aşık, O., & Akın, H. L. (2013). Solving multi-agent decision problems modeled as Dec-POMDP: A robot soccer case study. In RoboCup 2012: Robot Soccer World Cup XVI (pp. 130–140). Berlin: Springer.
Wang, S., Mao, Z., Zeng, C., Gong, H., Li, S., & Chen, B. (2010). A new method of virtual reality based on Unity3D. In 2010 18th International Conference on Geoinformatics (pp. 1–5). IEEE.
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 19.
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Ivanov, M. et al. (2020). Data Exchange and Task of Navigation for Robotic Group. In: Sergiyenko, O., Flores-Fuentes, W., Mercorelli, P. (eds) Machine Vision and Navigation. Springer, Cham. https://doi.org/10.1007/978-3-030-22587-2_13
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