Abstract
The paper presents the estimation method for operational environment complexity by a robotic team. Its distinguishing feature is the way to organize the intragroup information exchange between the team members, identifying the robots that, due to some restrictions, are not able to adequately estimate the complexity of the surrounding area. Overcoming the restrictions implies assistance from group members, meanwhile, their reasons may be of different origin, for example, faulty individual sensors or sensors’ visibility range being overlapped with environmental elements.
Upon the method application, an unambiguous (explicit) idea concerning the information sources and receivers and their necessary list, is formed, which makes the exchange of data more efficient and the estimation process more accurate.
A special function set has been developed for environment complexity estimation; using this function set implies representing the surrounding area as a plane divided into sectors with their dimensions corresponding to the team robot size. The transition from three-dimensional to two-dimensional space is carried out by projecting onto a flat surface the coordinates of the environment elements.
The presented function set is approximated with high precision by an artificial neural network that can be used for further evaluations.
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Mohanarajah, G., Usenko, V., Singh, M., D’Andrea, R., Waibel, M.: Cloud-based collaborative 3D mapping in real-time with low-cost robots. IEEE Trans. Autom. Sci. Eng. 423–431 (2015). https://doi.org/10.1109/TASE.2015.2408456. ISSN 1545-5955
Peter, F., et al.: Collaborative navigation for flying and walking robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2859–2866 (2016). https://doi.org/10.3929/ethz-a-010687710
Gadd, M., Newman, P.: Checkout my map: version control for fleetwide visual localisation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 5729–5736 (2016). https://doi.org/10.1109/IROS.2016.7759843
Contreras, L., Kermorgant, O., Martinet, P.: Efficient decentralized collaborative mapping for outdoor environments. In: International Conference on Robotic Computing, Laguna Hills, United States, January 2018. https://doi.org/10.1109/IRC.2018.00017
Jessup, J., Givigi, S.N., Beaulieu, A.: Merging of octree based 3D occupancy grid maps. In: IEEE International Systems Conference Proceedings Ottawa, ON, Canada, April 2014. https://doi.org/10.1109/SysCon.2014.6819283
Gulli, A., Pal, S.: Deep learning with Keras. Implement Neural Networks with Keras on Therano and TensorFlow/translated from English by A. A. Slinkin. DMK Press (2018). 294 p. illustated
Joseph, L.: Mastering ROS for Robotics Programming. Packt Publishing (2015). ISBN 978-1-78355-179-8
Fairchild, C., Harman, T.L.: ROS Robotics by Example. Packt Publishing (2016). ISBN 978-1-78217-519-3
Krinkin, K., Filatov, A., Filatov, A.: Modern multi-agent SLAM approaches survey. In: Proceedings of the FRUCT20, 776 p (2017). ISSN 2305-7254, ISBN 978-952-68653-0-0
Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016). https://doi.org/10.1109/TRO.2016.2624754
Dubé, R., Cramariuc, A., Dugas, D., Nieto, J., Siegwart, R., Cadena, C.: SegMap: 3D Segment Mapping using Data-Driven Descriptors. https://arxiv.org/abs/1804.09557 (2017)
Bloesch, M., Omari, S., Hutter, M., Siegwart, R.: ROVIO: robust visual inertial odometry using a direct EKF-based approach. In: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS) (2015)
Acknowledgment
The study is supported by the Russian Science Foundation grant No. 18-19-00621 at Joint stock Company «Scientific-Design bureau of Robotics and Control Systems».
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Beloglazov, D.A., Vasileva, M.A., Soloviev, V.V., Pereverzev, V.A., Pshihopov, V.H. (2021). Estimation Method for Operational Environment Complexity by a Robotic Team. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_16
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DOI: https://doi.org/10.1007/978-3-030-79463-7_16
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