Hierarchical task decomposition approach to path planning and control for an omni-directional autonomous mobile robot

Abstract

Describes a multi-resolution behavior generation strategy for a novel six-wheel omni-directional autonomous robot. The strategy is characterized by a hierarchical task decomposition approach. At the supervisory level a knowledge-based planner and an A*-optimization algorithm are used to specify the vehicle's path as a sequence of basic maneuvers. At the vehicle level these basic maneuvers are converted to time-domain trajectories. These trajectories are then tracked in an inertial reference frame using a model-based feedback linearization controller that computes set points for each wheel's low-level drive motor and steering angle motor controllers. The effectiveness of the strategy is demonstrated in actual tests with a real robot in which the path planning and control algorithms are implemented in a distributed processing environment.