Earticle

초록

영어

This article examines navigation of a flying robot inside a building environment in three dimensional spaces in which the size and location of some obstacles are not determined and other obstacles and target can be moving. This article suggests a new method by combining Q-learning algorithm and Monte Carlo algorithm on optimal navigation by the flying robot. The rewards are intended to be maximized when the robot flies in the right route; moreover, the maximum performance power would be measured according to the future predictions and the well-doing of that action would be also measured. Here, this method has been implemented with Webots simulator, and simulated data are analyzed by MATLAB. The simulation results show that control of the policy obtained from Q-learning and Monte Carlo methods is more efficient compared to traditional methods in controlling flying robot navigation.

목차

Abstract
 1. Introduction
 2. Literature Review
 3. Kinematics and Robot Model
 4. Hybrid Algorithm of Q-learning and Monte Carlo
  4.1. Q-learning 
  4.2. Mont Carlo Method
 5. Hybrid Algorithm
 6. Results of Combined Learning Simulation in Webots
  6.1. Implementation of Webots Simulation
  6.2. Analysis of Hybrid Learning Simulation Results in MATLAB
 7. Conclusion
 References

키워드

저자

• Sima Vosoghi Asl [ Technical and vocational university of Sabzevar academy, Iran ]
• Zohreh Davarzani [ Department of computer engineering, Payame Noor University, 19395-4697, Tehran, Iran ]
• Soheila Staji [ Technical and vocational university of Sabzevar academy, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605