Neural Network Vision-Guided Mobile Robot for Retrieving Driving-Range Golf Balls

2006 ◽  
Vol 10 (2) ◽  
pp. 181-186 ◽  
Author(s):  
Elmer P. Dadios ◽  
◽  
Kaoru Hirota ◽  
Michelle L. Catigum ◽  
Albert C. Gutierrez ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Open Field ◽  
Vision System ◽  
Time Of Day ◽  
Autonomous Mobile Robot ◽  
Rotating Blades ◽  
Driving Range

We developed an autonomous mobile robot with neural network (NN) vision that searches for and collects golf balls on an open or an indoor golf driving range. The robot recognizes range borderlines by red stripes. Scattered golf balls are collected using mechanically designed rotating blades. The NN vision identifies objects that are not golf balls and prevents the robot from picking them. The vision system is robust enough to navigate an open field and pick up the golf balls any time of day. Results of the experiments showed that our proposal operates accurately and reliably.

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.

scholarly journals Navigation of autonomous mobile robot using different activation functions of wavelet neural network

2015 ◽  
Vol 25 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Pratap Kumar Panigrahi ◽  
Saradindu Ghosh ◽  
Dayal R. Parhi
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Research Area ◽  
Initial Position ◽  
Wavelet Neural Network ◽  
Autonomous Mobile Robot ◽  
Activation Functions ◽  
Navigation Problem ◽  
Learning Speed ◽  
Mexican Hat

Abstract An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network.

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