TY - CONF T1 - Robust Control of Autonomous Vehicle Trajectories T2 - AIMS Y1 - 2016 A1 - Jonathan Sprinkle A1 - Rahul Bhadani A1 - Shumo Cui A1 - Benjamin Seibold AB -

In this paper we describe a robust treatment of tracking trajectories with an autonomous vehicle. In employing autonomous behaviors for traffic control there will inevitably be disturbances introduced through model error, non-planar surfaces, sensor noise, and delay in both sensing and actuation. We describe how we address these issues through robust control techniques. The trajectories we follow include position and orientation as part of their specification: but the most interesting aspect of these trajectories is the time-varying description of the state. This is opposed to a traditional approach of following a trajectory at any speed (with expected error in all dimensions of the state vector), as long as the speed does not exceed a maximum value. However, for traffic control to reduce traffic waves, most of the dampening approaches are time-varying trajectories. With this in mind, it becomes necessary to consider the delay of following the reference trajectory, and how this may affect drivers in the flow. We include simulation data demonstrating the results, as well as data from a full-sized robotic Ford Escape.

JF - AIMS ER -