Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

---

Applying supervised learning in robot navigation encounters serious challenges such as inconsistence and noisy data, difficulty to gathering training data, and high error in training data. Reinforcement Learning (RL) capabilities such as lack of need to training data, training using only a scalar evaluation of efficiency and high degree of exploration have encourage researcher to use it in robot navigation problem. However, RL algorithms are time consuming also have high failure rate in the training phase. Here, a novel idea for utilizing advantages of both above supervised and reinforcement learning algorithms is proposed. A zero order Takagi-Sugeno (T-S) fuzzy controller with some candidate actions for each rule is considered as robot controller. The aim of training is to find appropriate action for each rule. This structure is compatible with Fuzzy Sarsa Learning (FSL) which is used as a continuous RL algorithm. In the first step, the robot is moved in the environment by a supervisor and the training data is gathered. As a hard tuning, the training data is used for initializing the value of each candidate action in the fuzzy rules. Afterwards, FSL fine-tunes the parameters of conclusion parts of the fuzzy controller online. The simulation results in KiKS simulator show that the proposed approach significantly improves the learning time, the number of failures, and the quality of the robot motion.

Keywords: Robot navigation, Supervised learning, Reinforcement learning, Fuzzy controller

Full-Text [PDF 487 kb]   (6151 Downloads)    

Add your comments about this article : Your username or Email:

---

---