Abstract
In the coupled tasks assignment, the lack of the continuity between coupled tasks leads to the inefficient assignment. This paper presents an effective consensus assignment algorithm to solve the problem. Based on the concept of task bundle, the coupled tasks assignment model is established. Bidding decision variable is utilized to solve timing constraints problem between coupled tasks, and task scoring scheme is built by combining the information entropy and the length increment of UAV flight path. Then in the asynchronous setting, the extending consensus rules of the coupled tasks assignment is designed, which emphasizes the continuity and completeness of the coupled tasks, and guarantees the algorithm converges to a conflict-free assignment among the whole UAV team. The simulation results verify the validity of the presented algorithms with significantly faster convergence and fewer passed messages among UAVs in the asynchronous setting.
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References
Elloumi, S.: An efficient linearization for the constrained task allocation problem. Appl. Spectrosc. 56(9), 1170–1175 (2015)
Hu, J.W., Xie, L.H., Lum, K.J., et al.: Multi-agent information fusion and cooperative control in target search. IEEE Trans. Control Syst. Technol. 21(4), 1223–1235 (2013)
Zhao, W., Meng, Q., Chung, P.W.: 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)
Li, P., Duan, H.: A potential game approach to multiple UAV cooperative search and surveillance. Aerosp. Sci. Technol. 68, 403–415 (2017)
Choi, H.-L., Brunet, L., Jonathan, P.: Consensus-based decentralized auctions for robust task allocation. IEEE Trans. Robot. 25(4), 912–926 (2009)
Ernest, N.D., Garcia, E., Casbeer, D.: Multi-agent cooperative decision making using genetic cascading fuzzy systems. In: AIAA Infotech (2013)
Ernest, N.D., Garcia, E., Casbeer, D.: Genetic fuzzy trees and their application towards autonomous training and control of a squadron of unmanned combat aerial vehicles. Unmanned Syst. 3(03), 185–204 (2015)
Ernest, N.D., Garcia, E., Casbeer, D.: Learning of intelligent controllers for autonomous unmanned combat aerial vehicles by genetic cascading fuzzy methods. In: SAE Aerospace Systems & Technology Conference (2014)
Choi, H.-L., Whitten, A.K., How, J.P.: Decentralized task allocation for heterogeneous teams with cooperation constraints. In: American Control Conference, vol. 58, no. 8, pp. 3057–3062 (2010)
Gao, C., Zhen, Z., Gong, H.: a self-organized search and attack algorithm for multiple unmanned aerial vehicles. Aerosp. Sci. Technol. 54, 229–240 (2016)
Lee, W., Bang, H., Leeghim, H.: Cooperative localization between small UAVs using a combination of heterogeneous sensors. Aerosp. Sci. Technol. 27(1), 105–111 (2013)
McIntyre, G.A., Hintz, K.J.: An information theoretic approach to sensor scheduling. In: Signal Proceedings of the Sensor Fusion and Target Recognition V. Bellingham: SPIE, pp. 304–312 (1996)
Johnson, L.: Decentralized Task Allocation for Dynamic Environments. Master’s thesis, Massachusetts Institute of Technology (2012)
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Liu, Y., Zhu, J. (2023). Distributed Multi-UAVs Coupled Tasks Consensus Assignment. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_361
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DOI: https://doi.org/10.1007/978-981-99-0479-2_361
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