Abstract
Cooperative monitoring targets of mobile robots is of great importance in military, civil, and medical applications. In order to achieve multi-robot coordinated monitoring, this paper proposes a new distributed path planning algorithm. In this algorithm, a method of dynamic sequential decision making is used firstly to determine which robots should be moved to the neighbourhoods of the target path. A distributed cooperative path planning algorithm is then proposed in which the robots can only utilize their local information to make the joint monitoring areas of robots cover the target path. To reduce the chance of detection and ensure enough time to implement monitoring handover, our distributed algorithm can guarantee enough overlapped monitoring areas between adjacent robots. The effectiveness of the algorithm was verified by simulation experiments and a comparison experiment in typical scenarios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chakrabarty, K., Iyengar, S.S., Qi, H., et al.: Grid coverage for monitoring and target location in distributed sensor networks. IEEE Trans. Comput. 51(12), 1448–1453 (2002)
Chen, J., Zhang, X., Xin, B., Fang, H.: Coordination between unmanned aerial and ground vehicles: a taxonomy and optimization perspective. IEEE Trans. Cybern. 46(4), 959–972 (2016)
Eftekhari, M., Kranakis, E., Krizanc, D., et al.: Distributed algorithms for barrier coverage using relocatable sensors. Distrib. Comput. 29(5), 361–376 (2013)
Fu, Y., Ling, Q., Tian, Z.: Distributed sensor allocation for multi-target pathing in wireless sensor networks. IEEE Trans. Aerosp. Electron. Syst. 48(4), 3538–3553 (2012)
Khan, A., Rinner, B., Cavallaro, A.: Cooperative robots to observe moving targets: review. IEEE Trans. Cybern. 48(1), 187–198 (2016)
Qi, M.F., Dou, L.H., Xin, B., et al.: Optimal path planning for an unmanned aerial vehicle under navigation relayed by multiple stations to intercept a moving target. In: 56th IEEE Conference on Decision and Control, pp. 2744–2749 (2017)
Xin, B., Gao, G.Q., Ding, Y.L., et al.: Distributed multi-robot motion planning for cooperative multi-area coverage. In: IEEE International Conference on Control and Automation, pp. 361–366, August 2017
Zhang, X., Chen, J., Xin, B., et al.: A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets. Chin. J. Aeronaut. 27(3), 622–633 (2014)
Zhao, W., Meng, Q., Chung, P.W.H.: A heuristic distributed task allocation method for multivehicle multitask problems and its application to search and rescue scenario. IEEE Trans. Cybern. 46(4), 902–915 (2016)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 61673058, in part by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grant U1609214, in part by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621063, in part by the Projects of Major International (Regional) Joint Research Program NSFC under Grant 61720106011, and in part by International Graduate Exchange Program of Beijing Institute of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 IFIP International Federation for Information Processing
About this paper
Cite this paper
Jiang, J.Q., Xin, B., Dou, L.H., Ding, Y.L. (2018). Multi-robot Distributed Cooperative Monitoring of Mobile Targets. In: Shi, Z., Pennartz, C., Huang, T. (eds) Intelligence Science II. ICIS 2018. IFIP Advances in Information and Communication Technology, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-030-01313-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-01313-4_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01312-7
Online ISBN: 978-3-030-01313-4
eBook Packages: Computer ScienceComputer Science (R0)
