Collective motion pattern scaling for improved open-loop off-road navigation

Abstract

This paper presents an adaptive navigation system which is able to steer an electronically controlled ground vehicle to given destinations while it adjusts to changing surface conditions. The approach is designed for vehicles without a velocity controlled drive-train, making it especially useful for typical remote-controlled vehicles without upgraded motor controllers. The vehicle is controlled by sets of commands, each set representing a specific maneuver. These sets are combined to form trajectories towards a given destination. While one of these sets of commands is executed the vehicle's movement is measured to refine the geometry of all maneuvers. A scaling vector is derived from the changes in dimensions of the bounding boxes of the assumed and the actual path, which is then used to collectively update all known maneuvers. This enables the approach to quickly adapt to surface alterations. We tested our approach using a 300 kg Explosive Ordnance Disposal (EOD) robot in an outdoor environment. The experiments confirmed that the Collective Motion Pattern Scaling significantly increases the adaptation performance compared to an approach without collective scaling