Application of Fuzzy Behavior Coordination and Q Learning in Robot Navigation
نویسندگان
چکیده
Behavior based architecture is widely used in mobile robot because it makes the robot response faster. If robot only works to achieve simple task, it can use some primitive behaviors. However, when the task is getting more complex, the behavior coordination is needed. In order to construct this coordinator, fuzzy logic can be applied as Fuzzy Behavior Coordinator (FBC). By using FBC, it can be seen from simulation that robot has smoother movement and shorter time to reach target in its navigation. When the robot is in new and uncertain field, it needs to learn. Q learning can be used to give intelligence behavior to robot. Robot can construct its own behavior by learning from its environment. By applying Q learning, the shortest path to reach target will be obtained after some episodes of robot training.
منابع مشابه
A Q-learning Based Continuous Tuning of Fuzzy Wall Tracking
A simple easy to implement algorithm is proposed to address wall tracking task of an autonomous robot. The robot should navigate in unknown environments, find the nearest wall, and track it solely based on locally sensed data. The proposed method benefits from coupling fuzzy logic and Q-learning to meet requirements of autonomous navigations. Fuzzy if-then rules provide a reliable decision maki...
متن کاملBehavior Based Control and Fuzzy Q-learning for Autonomous Mobile Robot Navigation
This paper presents collaboration of behavior based control and fuzzy Q-learning for mobile robot navigation systems. There are many fuzzy Qlearning algorithms that have been proposed to yield individual behavior like obstacle avoidance, find target and so on. However, for complicated tasks, it is needed to combine all behaviors in one control schema using behavior based control. Based this fac...
متن کاملEmbedded Learning Robot with Fuzzy Q-learning for Obstacle Avoidance Behavior
Fuzzy Q-learning is extending of Q-learning algorithm that uses fuzzy inference system to enable Qlearning holding continuous action and state. This learning has been implemented in various robot learning application like obstacle avoidance and target searching. However, most of them have not been realized in embedded robot. This paper presents implementation of fuzzy Q-learning for obstacle av...
متن کاملUsing Q-Learning and Fuzzy Q-Learning Algorithms for Mobile Robot Navigation in Unknown Environment
One of the standing challenging aspects in mobile robotics is the ability to navigate autonomously. It is a difficult task, which requiring a complete modeling of the environment. This paper presents an intelligent navigation method for an autonomous mobile robot which requires only a scalar signal like a feedback indicating the quality of the applied action. Instead of programming a robot, we ...
متن کاملBehaviors Coordination and Learning on Autonomous Navigation of Physical Robot
Behaviors coordination is one of keypoints in behavior based robotics. Subsumption architecture and motor schema are example of their methods. In order to study their characteristics, experiments in physical robot are needed to be done. It can be concluded from experiment result that the first method gives quick, robust but non smooth response. Meanwhile the latter gives slower but smoother res...
متن کامل