Vector Pursuit Path Tracking for Autonomous Ground Vehicles

摘要

The Air Force Research Laboratory at Tyndall Air Force Base, Florida, has contracted the University of Florida to develop autonomous navigation for various ground vehicles. Autonomous vehicle navigation can be broken down into four tasks. These tasks include perceiving and modeling the environment, localizing the vehicle within the environment, planning and deciding the vehicle's desired motion, and finally, executing the vehicle's desired motion. The work presented here focuses on tasks of deciding the vehicle's desired motion and executing the vehicle's desired motion. The third task above involves planning the vehicle's desired motion as well as deciding the vehicle's desired motion. In this work it is assumed that a planned path already exists and therefore only a technique to decide the vehicle's desired motion is required. Screw theory can be used to describe the instantaneous motion of a rigid body, i.e., the vehicle, relative to a given coordinate system. The concept of vector pursuit is to calculate an instantaneous screw that describes the motion of the vehicle from its current position and orientation to a position and orientation on the planned path. Once the desired motion is determined, a controller is required to track this desired motion. The fourth task for autonomous navigation is to execute the desired motion. In order to accomplish this task, two fuzzy reference model learning controllers (FRMLCs) are implemented to execute the vehicle s desired turning rate and speed. The controllers are designed to be dependent on certain vehicle characteristics such as the maximum vehicle speed maximum turning rate. This is done to facilitate the transfer of these controllers to different vehicles. The vector pursuit path-tracking method and the FRMLCs were first tested in simulation by modeling the Navigation Test Vehicle (NTV) developed by the Center for Intelligent Machines and Robotics (CIMAR) at the University of Florida.