Enhancement of the Texas A&M University Autonomous Underwater Vehicle Controller through development of a middle level classical path planner

Abstract

The Texas A&M University Autonomous Underwater Vehicle Controller (AUVC) is a combination of software and hardware that provides real-time, fault-tolerant control of an unmanned, untethered submersible. The collision avoidance controller (CAC) is the reactive path planning component of the AUVC which performs real-time obstacle detection, tracking and avoidance from raw sonar data. This module relies on a merit function based on the concept of potential fields for path planning. The problem of false minima is addressed through the use of a visit count which effectively builds up the potential basin of a false minimum into a repulsive mound. The product of this research is a higher level classical path planner designed to assist the merit function in complicated environments which require backtracking or directed search for a valid path. The specific safe travel requirements of the prototype AUV allow reduction of the path planning problem for the AUV to that of path planning for a point robot. An octree model of the environment is constructed from the sonar data processed by the CAC and an A* search of this octree produces a list of subgoals used to modify the avoidance behavior of the merit function. This higher level path planner is resolution complete in static environments. This fact and the current near real-time, correct performance of the octree modeler and planner affirm the validity of this approach.