DocumentCode :
3338074
Title :
Reinforcement learning with heuristic to solve POMDP problem in mobile robot path planning
Author :
Adiprawita, Widyawardana ; Ahmad, A.S. ; Sembiring, Jaka ; Trilaksono, B. Riyanto
Author_Institution :
Sch. of Electr. Eng. & Inf., Bandung Inst. of Technol., Bandung, Indonesia
fYear :
2011
fDate :
17-19 July 2011
Firstpage :
1
Lastpage :
5
Abstract :
In this paper we propose a method of presenting a special case of Value Function as a solution to POMDP in mobile robot navigation. By using this new method the Value Function complexity will be reduced and more intuitive. We also propose a new reinforcement learning method to solve the Value Function. This reinforcement learning is based on Bellman Equation augmented with A* like heuristic during update iteration. The result of this new Value Function is validated with This particle filter is simulaed in Matlab and also experimented physically using a simple autonomous mobile robot built with Lego Mindstorms NXT with 3 ultrasonic sonar and RWTH Mindstorms NXT Toolbox for Matlab to connect the robot to Matlab. This simulation and experiment also incorporate particle filter localization from previous research. The simulation and experiment show that the Value Function can be utilized very well.
Keywords :
learning (artificial intelligence); mobile robots; particle filtering (numerical methods); path planning; Bellman Equation; Lego Mindstorms NXT; Matlab; POMDP problem; RWTH Mindstorms NXT Toolbox; mobile robot navigation; mobile robot path planning; particle filter localization; reinforcement learning; ultrasonic sonar; value function; Educational institutions; Equations; Mobile robots; Probabilistic logic; Robot sensing systems; Uncertainty; LEGO Mindstorm NXR; POMDP; RWTH toolbox; autonomous mobile robot; navigation; value function; value iteration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering and Informatics (ICEEI), 2011 International Conference on
Conference_Location :
Bandung
ISSN :
2155-6822
Print_ISBN :
978-1-4577-0753-7
Type :
conf
DOI :
10.1109/ICEEI.2011.6021734
Filename :
6021734
Link To Document :
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