Analytic error variance predictions for planar vehicles

Authors:
Matthew Greytak;Franz Hover
Affiliations:
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA;Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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Maneuver-based motion planning for nonlinear systems with symmetries

IEEE Transactions on Robotics

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Abstract

Path planning algorithms that incorporate risk and uncertainty need to be able to predict the evolution of path-following error statistics for each candidate plan. We present an analytic method to predict the evolving error statistics of a holonomic vehicle following a reference trajectory in a planar environment. This method is faster than integrating the plant through time or performing a Monte Carlo simulation. It can be applied to systems with external Gaussian disturbances, and it can be extended to handle plant uncertainty through numerical quadrature techniques.