Robot path planning algorithm based on partially observable Markov

2017 ◽  
pp. 1-6 ◽  
Author(s):  
Yanpeng Feng ◽  
Bo Wu
Keyword(s):  
Path Planning ◽  
Robot Path Planning ◽  
Planning Algorithm ◽  
Partially Observable Markov ◽  
Partially Observable ◽  
Path Planning Algorithm ◽  
Robot Path

A fast robot path planning algorithm based on bidirectional associative learning

2021 ◽  
Vol 155 ◽  
pp. 107173
Author(s):  
Meng Zhao ◽  
Hui Lu ◽  
Siyi Yang ◽  
Yinan Guo ◽  
Fengjuan Guo
Keyword(s):  
Path Planning ◽  
Associative Learning ◽  
Robot Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

scholarly journals Multi-Robot Path Planning Method Using Reinforcement Learning

2019 ◽  
Vol 9 (15) ◽  
pp. 3057 ◽  
Author(s):  
Hyansu Bae ◽  
Gidong Kim ◽  
Jonguk Kim ◽  
Dianwei Qian ◽  
Sukgyu Lee
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Robot Path Planning ◽  
Q Learning ◽  
Efficient Performance ◽  
Planning Algorithm ◽  
Neural Network Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path ◽  
Multi Robot

This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.

Sign in / Sign up

Export Citation Format

Share Document