Analytic error variance predictions for planar vehicles

— Path planning algorithms that incorporate risk and uncertainty need to be able to predict the evolution of pathfollowing 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.

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Matthew Greytak, Franz Hover
• Keywords: Error Statistics, External Gaussian Disturbances, ICRA 2009, Path Planning Algorithms, Robotics