Autonomous Convoy Study of Unmanned Ground Vehicles using Visual Snakes

Abstract

Many applications for unmanned vehicles involve autonomous interaction between two or more craft, and therefore, relative navigation is a key issue to explore. Several high fidelity hardware simulations exist to produce accurate dynamics. However, these simulations are restricted by size, weight, and power needed to operate them. The use of a small Unmanned Ground Vehicle (UGV) for the relative navigation problem is investigated. The UGV has the ability to traverse large ranges over uneven terrain and into varying lighting conditions which has interesting applications to relative navigation. The basic problem of a vehicle following another is researched and a possible solution explored. Statistical pressure snakes are used to gather relative position data at a specified frequency. A cubic spline is then fit to the relative position data using a least squares algorithm. The spline represents the path on which the lead vehicle has already traversed. Controlling the UGV onto this relative path using a sliding mode control, allows the follow vehicle to avoid the same stationary obstacles the lead vehicle avoided without any other sensor information. The algorithm is run on the UGV hardware with good results. It was able to follow the lead vehicle around a curved course with only centimeter-level position errors. This sets up a firm foundation on which to build a more versatile relative motion platform.