<abstract xmlns="http://www.w3.org/1999/xhtml"><p> An autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core strategies are the use of tendency cell and cognitive learning cell, which simulate bionic orientation and asymptotic learning ability. Cognitive learning cell is designed on the base of Boltzmann machine and improved Q-Learning algorithm, which executes operant action learning function to approximate the operative part of robot system. The tendency cell adjusts network weights by the use of information entropy to evaluate the function of operate action. The results of the simulation experiment in mobile robot showed that the designed autonomous path-planning strategy lets the robot realize autonomous navigation path planning. The robot learns to select autonomously according to the bionic orientate action and have fast convergence rate and higher adaptability.</p></abstract>

An autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core strategies are the use of tendency cell and cognitive learning cell, which simulate bionic orientation and asymptotic learning ability. Cognitive learning cell is designed on the base of Boltzmann machine and improved Q-Learning algorithm, which executes operant action learning function to approximate the operative part of robot system. The tendency cell adjusts network weights by the use of information entropy to evaluate the function of operate action. The results of the simulation experiment in mobile robot showed that the designed autonomous path-planning strategy lets the robot realize autonomous navigation path planning. The robot learns to select autonomously according to the bionic orientate action and have fast convergence rate and higher adaptability.

Autonomous Path Planning Scheme Research for Mobile Robot

School of Electronic and Control Engineering, Beijing University of Technology, No 100, Pingleyuan, Chaoyang District, 100124 Beijing, China

Department of Disaster Prevention Instrument, Institute of Disaster Prevention, No 3, University Street, Yanjiao Development Zone, Sanhe, 065201 Hebei,

Beijing Key Laboratory of Computational Intelligence and Intelligent System, No 100, Pingleyuan, Chaoyang District, 100124 Beijing,

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Autonomous Path Planning Scheme Research for Mobile Robot"}

An autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core...

Autonomous Path Planning Scheme Research for Mobile Robot

Online veröffentlicht: 22. Dez. 2016

Seitenbereich: 113 - 125

DOI: https://doi.org/10.1515/cait-2016-0072

Schlüsselwörter
Autonomous path planning, cognitive learning, information entropy, Q-Learning, mobile robots

© by Jianxian Cai

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Autonomous Path Planning Scheme Research for Mobile Robot

Online veröffentlicht: 22. Dez. 2016

Seitenbereich: 113 - 125

DOI: https://doi.org/10.1515/cait-2016-0072

SchlüsselwörterAutonomous path planning, cognitive learning, information entropy, Q-Learning, mobile robots

© by Jianxian Cai

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Autonomous path planning, cognitive learning, information entropy, Q-Learning, mobile robots