A Distributed Model Predictive Control Framework for Road-Following Formation Control of Car-like Vehicles (Extended Version)

Abstract : This work presents a novel framework for the formation control of multiple autonomous ground vehicles in an on-road environment. Unique challenges of this problem lie in 1) the design of collision avoidance strategies with obstacles and with other vehicles in a highly structured environment, 2) dynamic reconfiguration of the formation to handle different task specifications. In this paper, we design a local MPC-based tracking controller for each individual vehicle to follow a reference trajectory while satisfying various constraints (kinematics and dynamics, collision avoidance, etc.). The reference trajectory of a vehicle is computed from its leader's trajectory, based on a pre-defined formation tree. We use logic rules to organize the collision avoidance behaviors of member vehicles. Moreover, we propose a methodology to safely reconfigure the formation on-the-fly. The proposed framework has been validated using high-fidelity simulations.
Type de document :
Pré-publication, Document de travail
2016
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https://hal.archives-ouvertes.fr/hal-01309659
Contributeur : Xiangjun Qian <>
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Dernière modification le : lundi 12 novembre 2018 - 10:57:49
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  • HAL Id : hal-01309659, version 1

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Xiangjun Qian, Florent Altché, Arnaud De La Fortelle, Fabien Moutarde. A Distributed Model Predictive Control Framework for Road-Following Formation Control of Car-like Vehicles (Extended Version). 2016. 〈hal-01309659〉

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