Title:
Multistrategy Learning of Adaptive Reactive Controllers
Multistrategy Learning of Adaptive Reactive Controllers
Author(s)
Santamaria, Juan Carlos
Ram, Ashwin
Ram, Ashwin
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Abstract
Reactive controllers has been widely used in mobile robots since 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 "learning adaptive
reactive controllers". 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. Furthermore,
learning adaptive reactive controllers can learn when and how to adapt the
reactive controller so as to achieve effective performance under different
conditions. The paper presents an algorithm for a learning adaptive
reactive controller that combines ideas from case-based reasoning and
reinforcement learning to construct a mapping between the operating
conditions of a controller and the appropriate controller configuration;
this mapping is in turn used to adapt the controller configuration
dynamically. As a case study, the algorithm is implemented in a robotic
navigation system that controls a Denning MRV-III mobile robot. The system
is extensively evaluated using statistical methods to verify its learning
performance and to understand the relevance of different design parameters
on the performance of the system.
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Date Issued
1997
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845955 bytes
Resource Type
Text
Resource Subtype
Technical Report