Path planning for mobile robot using sonar map and neural network

1998 ◽  
Author(s):  
Jin Cao ◽  
Wen-chuan Chiang ◽  
Terrell N. Mundhenk ◽  
Ernest L. Hall
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Mobile Robot

Local Path Planning of Mobile Robot Based on Long Short-Term Memory Neural Network

2021 ◽  
Vol 55 (1) ◽  
pp. 53-65
Author(s):  
Na Guo ◽  
Caihong Li ◽  
Di Wang ◽  
Yong Song ◽  
Guoming Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Mobile Robot ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Local Path Planning ◽  
Long Short Term Memory ◽  
Local Path

Cognition of a Robotic Manipulator Using the Q-Learning Based Situation-Operator Model

2018 ◽  
Vol 11 (1) ◽  
pp. 146-157 ◽  
Author(s):  
Akash Dutt Dubey ◽  
Ravi Bhushan Mishra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Path Planning ◽  
Mobile Robot ◽  
Knowledge Base ◽  
Operator Model ◽  
Q Learning ◽  
Sensory Inputs ◽  
Learning Module ◽  
Som Model

In this article, we have applied cognition on robot using Q-learning based situation operator model. The situation operator model takes the initial situation of the mobile robot and applies a set of operators in order to move the robot to the destination. The initial situation of the mobile robot is defined by a set of characteristics inferred by the sensor inputs. The Situation-Operator Model (SOM) model comprises of a planning and learning module which uses certain heuristics for learning through the mobile robot and a knowledge base which stored the experiences of the mobile robot. The control and learning of the robot is done using q-learning. A camera sensor and an ultrasonic sensor were used as the sensory inputs for the mobile robot. These sensory inputs are used to define the initial situation, which is then used in the learning module to apply the valid operator. The results obtained by the proposed method were compared to the result obtained by Reinforcement-Based Artificial Neural Network for path planning.

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