TY - GEN

T1 - A Hybrid Fuzzy Q-learning algorithm for robot navigation

AU - Gordon, Sean W.

AU - Reyes, Napoleon H.

AU - Barczak, Andre

PY - 2011

Y1 - 2011

N2 - In the field of robot navigation, a number of different approaches have been proposed. One of these is Hybrid Fuzzy A* (HFA), which uses the A* algorithm to determine the long term path from the robot to some target, and fuzzy logic to move the robot to each waypoint along the path. This algorithm has been shown to be fast and effective in simulation, however A* is limited in the variables it can consider and the challenges it can be applied to. We propose replacing A* with Q-learning, which does not suffer from these limitations. We demonstrate the ability of Hybrid Fuzzy Q-Learning (HFQL) to navigate a robot to a given target and then apply the algorithm to a different challenge where the robot needs to balance reaching the target quickly against picking up as many subgoals as possible.

AB - In the field of robot navigation, a number of different approaches have been proposed. One of these is Hybrid Fuzzy A* (HFA), which uses the A* algorithm to determine the long term path from the robot to some target, and fuzzy logic to move the robot to each waypoint along the path. This algorithm has been shown to be fast and effective in simulation, however A* is limited in the variables it can consider and the challenges it can be applied to. We propose replacing A* with Q-learning, which does not suffer from these limitations. We demonstrate the ability of Hybrid Fuzzy Q-Learning (HFQL) to navigate a robot to a given target and then apply the algorithm to a different challenge where the robot needs to balance reaching the target quickly against picking up as many subgoals as possible.

UR - http://www.scopus.com/inward/record.url?scp=80054772273&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2011.6033561

DO - 10.1109/IJCNN.2011.6033561

M3 - Conference contribution

AN - SCOPUS:80054772273

SN - 9781457710865

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 2625

EP - 2631

BT - IJCNN 2011 Conference Proceedings

PB - IEEE

T2 - 2011 International Joint Conference on Neural Network

Y2 - 31 July 2011 through 5 August 2011

ER -