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A Study of a Trajectory Synthesis Method for a Cyclic Changeable Target in an Environment with Periodic Dynamics of Properties

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Cyber-Physical Systems: Advances in Design & Modelling

Abstract

Trajectory planning in a large dynamic environment is a computationally complex cyber-physical task. The chapter considers an environment with periodic dynamics that simulates the rhythm of the day and night. Robots move between two target points cyclically. To optimize the trajectory planning process, it is possible to use pre-calculated paths. The pre-calculated state space consists of the planned paths for environmental states that can be considered static for a given period of time. The planning of robot movement in such the state space is carried out using parts of the pre-calculated optimal trajectories for a certain time and criteria for the transition between them. The method and criteria are studied by simulating the robot movement on two fundamentally different realistic maps. The method allows to plan the trajectories asynchronously with the time of the beginning of the movement of the robot, as well as to estimate the energy costs of overcoming the route.

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References

  1. Singh, Y., Sharma, S., Sutton, R., Hatton, D., Khan, A.: Feasibility study of a constrained Dijkstra approach for optimal path planning of an unmanned surface vehicle in a dynamic maritime environment. In: IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 117–122. Torres Vedras (2018). https://doi.org/10.1109/icarsc.2018.8374170

  2. Sadiq, A., Hasan, A.: Robot path planning based on PSO and D∗ algorithms in dynamic environment. In: 2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT), pp. 145–150. Slemani (2017). https://doi.org/10.1109/crcsit.2017.7965550

  3. Chen, S., Yang, Z., Liu, Z., Jin, H.: An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments. In: 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pp. 591–596. Shenzhen (2017). https://doi.org/10.1109/spac.2017.8304346

  4. Primatesta, S., Russo, L., Bona, B.: Dynamic trajectory planning for mobile robot navigation in crowded environments. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. Berlin (2016). https://doi.org/10.1109/etfa.2016.7733510

  5. Biswas, S., Anavatti, S., Garratt, M.: Nearest neighbour based task allocation with multi-agent path planning in dynamic environments. In: 2017 International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA), pp. 181–186. Surabaya (2017). https://doi.org/10.1109/icamimia.2017.8387582

  6. Tazir, M., Azouaoui, O., Hazerchi, M., Brahimi, M.: Mobile robot path planning for complex dynamic environments. In: 2015 International Conference on Advanced Robotics (ICAR), pp. 200–206. Istanbul (2015). https://doi.org/10.1109/icar.2015.7251456

  7. Dang, A., Horn, J.: Formation adaptation control of autonomous robots in a dynamic environment. In: 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 3190–3195. Seville (2015). https://doi.org/10.1109/icit.2015.7125569

  8. Mohri, A., Yamamoto, M., Fukuda, S.: Collision free trajectory planning for multiple mobile robots in environment with periodic motion obstacle. In: Proceedings of the 1996 IEEE IECON. 22nd International Conference on Industrial Electronics, Control, and Instrumentation, vol. 3, pp. 1572–1576. Taipei, Taiwan (1996). https://doi.org/10.1109/iecon.1996.570627

  9. Iocchi, L., Marchetti, L., Nardi, D.: Multi-robot patrolling with coordinated behaviours in realistic environments, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2796–2801. San Francisco, CA (2011). https://doi.org/10.1109/iros.2011.6094844

  10. Ha, J., Choi, H.: Periodic sensing trajectory generation for persistent monitoring. In: 53rd IEEE Conference on Decision and Control, pp. 1880–1886. Los Angeles, CA (2014). https://doi.org/10.1109/cdc.2014.7039672

  11. Nilles, A., Becerra, I., LaValle, S.: Periodic trajectories of mobile robots. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3020–3026. Vancouver, BC (2017). https://doi.org/10.1109/iros.2017.8206140

  12. Nitsche, M., de Cristóforis, P., Kulich, M., Košnar, K.: Hybrid mapping for autonomous mobile robot exploration. In: Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, pp. 299–304. Prague (2011). https://doi.org/10.1109/idaacs.2011.6072761

  13. Stepan, P., Kulich, M., Preucil, L.: Robust data fusion with occupancy grid. In: IEEE Trans. Syst. Man Cybern. C (Applications and Reviews) 35(1), 106–115 (2005). https://doi.org/10.1109/tsmcc.2004.840048

    Article  Google Scholar 

  14. Hoang, V., Hernández, D., Hariyono, J., Jo, K.-H.: Global path planning for unmanned ground vehicle based on road map images. In: 2014 7th International Conference on Human System Interactions (HSI), pp. 82–87. Costa da Caparica (2014). https://doi.org/10.1109/hsi.2014.6860453

  15. Ort, T., Paull, L., Rus, D.: Autonomous vehicle navigation in rural environments without detailed prior maps. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2040–2047. Brisbane, QLD (2018). https://doi.org/10.1109/icra.2018.8460519

  16. Fentanes, J., Lacerda, B., Krajník, T., Hawes, N., Hanheide, M.: Now or later? Predicting and maximising success of navigation actions from long-term experience. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1112–1117. Seattle, WA (2015). https://doi.org/10.1109/icra.2015.7139315

  17. Lan, X., Schwager, M.: Rapidly exploring random cycles: persistent estimation of spatiotemporal fields with multiple sensing robots. IEEE Trans. Rob. 32(5), 1230–1244 (2016). https://doi.org/10.1109/tro.2016.2596772

    Article  Google Scholar 

  18. Jahn, A., Alitappeh, R., Saldaña, D., Pimenta, L., Santos, A., Campos, M.F.: Distributed multi-robot coordination for dynamic perimeter surveillance in uncertain environments. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 273–278. Singapore (2017). https://doi.org/10.1109/icra.2017.7989035

  19. Vonásek, V., Saska, M., Košnar, K., Přeučil, L.: Global motion planning for modular robots with local motion primitives. In: 2013 IEEE International Conference on Robotics and Automation, pp. 2465–2470. Karlsruhe (2013). https://doi.org/10.1109/icra.2013.6630912

  20. Awashima, Y., Fujii, H., Tamura, Y., Nagatani, K., Yamashita, A., Asama, H.: Safeness visualization of terrain for teleoperation of mobile robot using 3D environment map and dynamic simulator. In: 2017 IEEE/SICE International Symposium on System Integration (SII), pp. 194–200. Taipei (2017). https://doi.org/10.1109/sii.2017.8279211

  21. Mishra, M. et al.: Context-aware decision support for Anti-Submarine Warfare mission planning within a dynamic environment. IEEE Trans. Syst. Man. Cybern. Syst. https://doi.org/10.1109/tsmc.2017.2731957

  22. Akhmetov, B., Balgabayeva, L., Lakhno, V., Malyukov, V., Alenova, R., Tashimova, A.: Mobile platform for decision support system during mutual continuous investment in technology for smart city. In: Dolinina, O., Brovko, A., Pechenkin, V., Lvov, A., Zhmud, V., Kreinovich, V. (eds.) Recent Research in Control Engineering and Decision Making. ICIT 2019. Studies in Systems, Decision and Control, vol. 199. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12072-6_59

    Chapter  Google Scholar 

  23. Kravets, A., Fomenkov, S., Kravets, A.: Component-based approach to multi-agent system generation. In: Kravets, A., Shcherbakov, M., Kultsova, M., Iijima, T. (eds.) Knowledge-Based Software Engineering. JCKBSE 2014. Communications in Computer and Information Science, vol. 466. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11854-3_42

    Google Scholar 

  24. Motorin, D., Popov, S.: Multi-criteria path planning algorithm for a robot on a multi-layer map. Informatsionno-upravliaiushchie sistemy [Inf. Control Syst.] (3), 45–53 (2018) (In Russian). https://doi.org/10.15217/issn1684-8853.2018.3.45

    Article  Google Scholar 

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Acknowledgements

The reported study was funded by RFBR according to the research project № 18-29-03250.

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Correspondence to Dmitrii Motorin .

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Motorin, D., Popov, S., Glazunov, V., Chuvatov, M. (2020). A Study of a Trajectory Synthesis Method for a Cyclic Changeable Target in an Environment with Periodic Dynamics of Properties. In: Kravets, A., Bolshakov, A., Shcherbakov, M. (eds) Cyber-Physical Systems: Advances in Design & Modelling. Studies in Systems, Decision and Control, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-030-32579-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-32579-4_10

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