Q(λ) Based Vector Direction for Path Planning Problem of Autonomous Mobile Robots
This paper presents a novel algorithm to improve the efficiency of path planning for autonomous mobile robots. In an obstacle-free environment, the path planning of a robot is attained by following the vector direction from its current position to the goal position. In an obstacle environment, while following the vector direction, a robot has to avoid obstacles by rotating the moving direction. To accomplish the obstacle avoidance task for the mobile robot, the Q(λ) algorithm is employed to train the robot to learn suitable moving directions. Experimental results show that the proposed algorithm is soundness and completeness with a fast learning rate in the large environment of states and obstacles.
KeywordsReinforcement learning Q-learning Q(λ) algorithm Path planning Mobile robots
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (2010-0012609).
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