Learning of Parameter-Adaptive Reactive Controllers for Robotic Navigation

Abstract

Reactive controllers are widely used in mobile robots because they are able to achieve successful performance in real-time. However, the configuration of a reactive controller depends highly on the operating conditions of the robot and the environment; thus, a reactive controller configured for one class of environments may not perform adequately in another. This paper presents a formulation of parameter-adaptive reactive controllers. Parameter-adaptive reactive controllers inherit all the advantages of traditional reactive controllers, but in addition they are able to adjust themselves to the current operating conditions of the robot and the environment in order to improve task performance. Additionally, the paper describes a multistrategy learning algorithm that combines ideas from case-based reasoning and reinforcement learning to construct a mapping between the operating conditions of the mobile robot and the appropriate controller configuration; this mapping is in turn used to adapt the controller configuration dynamically. The algorithm is implemented and evaluated in a robotic navigation system that controls a Denning MRV-III mobile robot.