Switched Linear Multi-Robot Navigation Using Hierarchical Model Predictive Control

Chao Huang, Xin Chen, Yifan Zhang, Shengchao Qin, Yifeng Zeng, Xuandong Li

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Abstract

Multi-robot navigation control in the absence of reference trajectory is rather challenging as it is expected to ensure stability and feasibility while still offer fast computation on control decisions. The intrinsic high complexity of switched linear dynamical robots makes the problem even more challenging. In this paper, we propose a novel HMPC based method to address the navigation problem of multiple robots with switched linear dynamics. We develop a new technique to compute the reachable sets of switched linear systems and use them to enable the parallel computation of control parameters. We present theoretical results on stability, feasibility and complexity of the proposed approach, and demonstrate its empirical advance in performance against other approaches.
Original languageEnglish
Publication statusPublished - 19 Aug 2017
EventInternational Joint Conference on Artificial Intelligence 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Conference

ConferenceInternational Joint Conference on Artificial Intelligence 2017
Abbreviated titleIJCAI 2017
CountryAustralia
CityMelbourne
Period19/08/1725/08/17

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  • Cite this

    Huang, C., Chen, X., Zhang, Y., Qin, S., Zeng, Y., & Li, X. (2017). Switched Linear Multi-Robot Navigation Using Hierarchical Model Predictive Control. Paper presented at International Joint Conference on Artificial Intelligence 2017, Melbourne, Australia.