scholarly journals Applications of Pulsed Neural Network Incorporating Short Term Synaptic Plasticity to Autonomous Mobile Robots

2007 ◽  
Vol 127 (7) ◽  
pp. 1052-1060 ◽  
Author(s):  
Makoto Motoki ◽  
Seiichi Koakutsu ◽  
Hironori Hirata
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Mobile Robots ◽  
Autonomous Mobile Robots ◽  
Short Term

Person Searching Through an Omnidirectional Camera Using CNN in the Tsukuba Challenge

2018 ◽  
Vol 30 (4) ◽  
pp. 540-551 ◽  
Author(s):  
Shingo Nakamura ◽  
◽  
Tadahiro Hasegawa ◽  
Tsubasa Hiraoka ◽  
Yoshinori Ochiai ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Autonomous Mobile Robots ◽  
Laser Sensor ◽  
Autonomous Mobile Robot ◽  
Omnidirectional Camera ◽  
Public Road

The Tsukuba Challenge is a competition, in which autonomous mobile robots run on a route set on a public road under a real environment. Their task includes not only simple running but also finding multiple specific persons at the same time. This study proposes a method that would realize person searching. While many person-searching algorithms use a laser sensor and a camera in combination, our method only uses an omnidirectional camera. The search target is detected using a convolutional neural network (CNN) that performs a classification of the search target. Training a CNN requires a great amount of data for which pseudo images created by composition are used. Our method is implemented in an autonomous mobile robot, and its performance has been verified in the Tsukuba Challenge 2017.

Intelligent Control of Autonomous Soccer Robots Compensating for Missing Information

2005 ◽  
Vol 9 (3) ◽  
pp. 268-276 ◽  
Author(s):  
Hikari Fujii ◽  
◽  
Nobuyuki Kurihara ◽  
Kazuo Yoshida ◽  
Keyword(s):  
Mobile Robots ◽  
Intelligent Control ◽  
Visual Information ◽  
Short Term Memory ◽  
Human Memory ◽  
Autonomous Mobile Robots ◽  
Missing Information ◽  
Short Term ◽  
Term Memory ◽  
Soccer Robots

For autonomous mobile robots, visual information is used to recognize the environment. Although the acquisition of visual information is often disturbed in the real environment, it is necessary for a robot to act appropriately even if information is missing. We compensate for missing information for autonomous mobile robots by using short-term memory (STM) to make robots act appropriately. This method involves short-term memory and action selectors. Short-term memory is constructed based on the model of human memory and the forgetting curve used in cognitive science. These action selectors use compensated-for information and determine suitable action. One action selector consists of a neural network whose connection weights are learned by a genetic algorithm. Another selector is designed based on the designer's knowledge. These action selectors are switched based on reliability index of information. RoboCup Middle Size League soccer robots are used for demonstration. The experimental and simulation results show its effectiveness.

Spiking neural network-based target tracking control for autonomous mobile robots

2015 ◽  
Vol 26 (8) ◽  
pp. 1839-1847 ◽  
Author(s):  
Zhiqiang Cao ◽  
Long Cheng ◽  
Chao Zhou ◽  
Nong Gu ◽  
Xu Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Target Tracking ◽  
Tracking Control ◽  
Autonomous Mobile Robots ◽  
Spiking Neural Network
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