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Distributed coordinated path following using guiding vector fields

Weijia Yao, Héctor García de Marina, Zhiyong Sun, Ming Cao

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Abstract

It is essential in many applications to impose a scalable coordinated motion control on a large group of mobile robots, which is efficient in tasks requiring repetitive execution, such as environmental monitoring. In this paper, we design a guiding vector field to guide multiple robots to follow possibly different desired paths while coordinating their motions. The vector field uses a path parameter as a virtual coordinate that is communicated among neighboring robots. Then, the virtual coordinate is utilized to control the relative parametric displacement between robots along the paths. This enables us to design a saturated control algorithm for a Dubins-car-like model. The algorithm is distributed, scalable, and applicable for any smooth paths in an n-dimensional configuration space, and global convergence is guaranteed. Simulations with up to fifty robots and outdoor experiments with fixed-wing aircraft validate the theoretical results.

Topics & Concepts

RobotConvergence (economics)Computer scienceScalabilityPath (computing)Parametric statisticsMobile robotVector fieldMotion planningDisplacement (psychology)Motion controlDistributed computingField (mathematics)Control theory (sociology)Control (management)Artificial intelligenceMathematicsDatabaseProgramming languageEconomicsGeometryPure mathematicsEconomic growthPsychologyStatisticsPsychotherapistDistributed Control Multi-Agent SystemsRobotic Path Planning AlgorithmsAdaptive Control of Nonlinear Systems
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