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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Acknowledgements
The reported study was funded by RFBR according to the research project № 18-29-03250.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-32579-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32578-7
Online ISBN: 978-3-030-32579-4
eBook Packages: EngineeringEngineering (R0)