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Flexible multi-unmanned ground vehicles (MUGVs) in intersection coordination based on ε-constraint probability collectives algorithm

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Abstract

Cooperative navigation (CN) is a widespread technique to have efficient navigation of intelligent vehicles. Nonetheless, the CN strategies need to be more consistent in estimating and managing in-road risks. This paper outlines a flexible CN scheme for multiple unmanned ground vehicles (MUGVs) system to deal with such critical cooperative system. With its relative low execution time, the probability collectives (PC) algorithm has succeeded at generating fast and feasible solutions to cross intersections and roundabouts (Philippe et al. 1928–1934, 2019). However, the PC is still sensitive to uncertainty in the navigation process, which highlights the need to adopt several safety margins. This work focuses on balancing between the high-quality cooperative optimization and acceptable computational speed. Thus, a reliable risk management strategy is proposed by introducing a novel ε-constraint PC method. A real-time communication mechanism is suggested for a distributed system to avoid invalid behavior due to inconsistency. The novel ε-PC based navigation strategy allows the vehicles to adapt their dynamics and react to unexpected events while respecting real-time constraints. One finding appears to be well substantiated by the typical common-yet-difficult scenarios in intensive simulations. The \(\varepsilon\)-PC method can ensure collision-free behaviors and reserve at least 1.5s of reaction time for vehicles’ safety insurance.

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Acknowledgements

This work has been sponsored by the Chinese Ministry of Industry and Information Technology (MIIT) research program the 2020 Innovative Development of the Industrial Internet (TC200H033 and TC200H01F), by the Dongfeng Motor Corporation project 928 (DF928-2020-040). This work received also the support of IMobS3 Laboratory of Excellence (ANR-10-LABX-16-01).

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Authors and Affiliations

  1. Institut Pascal, UMR CNRS 6602, Université Clermont Auvergne, 63178, Aubière Cedex, France

    Zhengze Zhu

  2. ASID, Hubei University of Automotive Technology, 442002, Shiyan, China

    Zhengze Zhu

  3. Heudiasyc, UMR CNRS 7253, Université de Technologie de Compiègne, 60203, Compiègne Cedex, France

    Lounis Adouane

  4. LIMOS, UMR CNRS 6158, Université Clermont Auvergne, 63178, Aubière Cedex, France

    Zhengze Zhu & Alain Quilliot

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  1. Zhengze Zhu
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  2. Lounis Adouane
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Correspondence to Zhengze Zhu.

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Zhu, Z., Adouane, L. & Quilliot, A. Flexible multi-unmanned ground vehicles (MUGVs) in intersection coordination based on ε-constraint probability collectives algorithm. Int J Intell Robot Appl 5, 156–175 (2021). https://doi.org/10.1007/s41315-021-00181-4

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