1A2-T03 Development of a Small sized Manipulation Mobile Robot That Switches a Task by Using the SD Cards : Veri cation of the Writing Performance of SD Card for Recording Robot Behavior(Mobile Manirulation Robot)

2012 ◽  
Vol 2012 (0) ◽  
pp. _1A2-T03_1-_1A2-T03_4
Author(s):  
Takuya KOMIZO ◽  
Shoichi MAEYAMA ◽  
Keigo WATANABE
Keyword(s):  
Mobile Robot ◽  
Writing Performance ◽  
Robot Behavior ◽  
Sd Card

Experimental Study on Behavior Acquisition of Mobile Robot by Deep Q-Network

2017 ◽  
Vol 21 (5) ◽  
pp. 840-848 ◽  
Author(s):  
Hikaru Sasaki ◽  
Tadashi Horiuchi ◽  
Satoru Kato ◽  
◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Visual Information ◽  
Profit Sharing ◽  
Simulation Software ◽  
Learning Performance ◽  
Simulation Environment ◽  
Q Learning ◽  
Robot Behavior ◽  
Target Network ◽  
Behavior Learning

Deep Q-network (DQN) is one of the most famous methods of deep reinforcement learning. DQN approximates the action-value function using Convolutional Neural Network (CNN) and updates it using Q-learning. In this study, we applied DQN to robot behavior learning in a simulation environment. We constructed the simulation environment for a two-wheeled mobile robot using the robot simulation software, Webots. The mobile robot acquired good behavior such as avoiding walls and moving along a center line by learning from high-dimensional visual information supplied as input data. We propose a method that reuses the best target network so far when the learning performance suddenly falls. Moreover, we incorporate Profit Sharing method into DQN in order to accelerate learning. Through the simulation experiment, we confirmed that our method is effective.

Truly-Tender-Tailed Tag-Playing Robot Interface Through Friendly Amusing Mobile Function

2010 ◽  
Vol 22 (3) ◽  
pp. 301-307 ◽  
Author(s):  
Takafumi Matsumaru ◽  
◽  
Yasutada Horiuchi ◽  
Kosuke Akai ◽  
Yuichi Ito
Keyword(s):  
Mobile Robot ◽  
Cognitive Functions ◽  
Software Package ◽  
Trajectory Design ◽  
Motion Trajectory ◽  
Step Detection ◽  
Robot Behavior ◽  
Behavior Design ◽  
Detection And Recognition

To expand use of the mobile robot Step-On Interface (SOI), originally targeting maintenance, training, and recovery of human physical and cognitive functions, we introduce a “Truly-Tender-Tailed” (T3, pronounced tee-cube) tag-playing robot as a “Friendly Amusing Mobile” (FAM) function. Displaying a previously prepared bitmap (BMP) image and speeding up display make it easy to design button placement and other screen parameters using a painting software package. The BMP-image scope matrix simplifies step detection and recognition and the motion trajectory design editor facilitates robot behavior design.

scholarly journals Using Bayesian Network for Robot Behavior with Sensor Fault

1970 ◽  
Vol 108 (2) ◽  
pp. 91-96
Author(s):  
A. Rezaee ◽  
A. Raie ◽  
A. Nadi ◽  
S. Shiry
Keyword(s):  
Mobile Robot ◽  
Bayesian Network ◽  
Bayesian Model ◽  
Sensor Fault ◽  
Real Environment ◽  
Robot Behavior ◽  
Learn Behavior

The paper discussed application of Bayesian network to learn behavior of mobile robot in presence of fault sensor. Theoretical and practical are considered for checking the results. Robot's model was considered as Bayesian model that each value of CPD was learned. This framework shows that can be work in real environment with noisy sensor. Ill. 12, bibl. 7, tabl. 2 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.152

Using Voting Technique In Mobile Robot Behavior Coordination For Goal-directed Navigation

2002 ◽  
Vol 36 (1) ◽  
Author(s):  
Chee Kwong Tan ◽  
Shamsudin H. M. Amin ◽  
Rosbi Mamat
Keyword(s):  
Mobile Robot ◽  
Robot Behavior

Autonomous Mobile Robot Behavior Control Using Immune Network

1998 ◽  
Vol 10 (4) ◽  
pp. 326-332 ◽  
Author(s):  
Yuji Watanabe ◽  
◽  
Akio Ishiguro ◽  
Yoshiki Uchikawa
Keyword(s):  
Mobile Robot ◽  
Biological Information ◽  
Immune Network ◽  
Autonomous Mobile Robot ◽  
Artificial Immune ◽  
Artificial Immune Network ◽  
Robot Behavior ◽  
Real Robot ◽  
Biological Information Processing ◽  
Simple Behavior

Attention has been increasingly focused on behaviorbased artificial intelligence (Al) due to its potential robustness and flexibility toward a dynamically changing world. This approach has yet unsolved problems: (1) how to construct an arbitration mechanism and (2) how to prepare competence modules (simple behavior/action). Biological information processing systems are interesting viewed from an engineering standpoint. Of these, we particularly have focused on the immune system, constructing a decentralized consensus-maker inspired by the immune network hypothesis. To solve the above problems in behavior-based Al, we apply our proposed method to behavior arbitration for an autonomous mobile robot in experiments using a real robot. We also study adaptation that automatically creates an artificial immune network using reinforcement signals.

KSU-IMR Mobile Robot Navigation Maps Building and Learning

2015 ◽  
Vol 789-790 ◽  
pp. 717-722
Author(s):  
Ebrahim Mattar ◽  
K. Al Mutib ◽  
M. AlSulaiman ◽  
Hedjar Ramdane
Keyword(s):  
Mobile Robot ◽  
Dimensionality Reduction ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Research Outcomes ◽  
Neuro Fuzzy ◽  
Robot Behavior ◽  
Behavior Learning ◽  
Mobile Maps

It is essential to learn a robot navigation environment. We describe research outcomes for KSU-IMR mapping and intelligence. This is for navigating and robot behavior learning. The mobile maps learning and intelligence was based on hybrid paradigms and AI functionaries. Intelligence was based on ANN-PCA for dimensionality reduction, and Neuro-Fuzzy architecture.

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