Neural network approach to acquiring free-gait motion of quadruped robots in obstacle avoidance

2005 ◽  
Vol 9 (4) ◽  
pp. 188-193 ◽  
Author(s):  
Tomohiro Yamaguchi ◽  
Keigo Watanabe ◽  
Kiyotaka Izumi
Keyword(s):  
Neural Network ◽  
Obstacle Avoidance ◽  
Network Approach ◽  
Neural Network Approach ◽  
Quadruped Robots

scholarly journals On the Performance Analyses of a Modified Force Field Algorithm and Neural Network Approach for Obstacle Avoidance in Swarm Robotics

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Girish Balasubramanian ◽  
Senthil Arumugam Muthukumaraswamy ◽  
Xianwen Kong
Keyword(s):  
Neural Network ◽  
Force Field ◽  
Obstacle Avoidance ◽  
Swarm Robotics ◽  
Field Method ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Swarm Robots ◽  
Swarm Robot

AbstractObstacle avoidance is a major hurdle when implementing mobile robots and swarm robots. Swarm robots work in groups and therefore require an efficient and functional obstacle avoidance algorithm to stay collision free between themselves and their surroundings. This paper reviews previous research in obstacle avoidance implementation using the force field method (FFM), also known as potential field method (PFM) and a neutral network approach. Moreover, this paper aims to execute simulations using a modified force field algorithm and a neural network approach and compare them. The obtained results are analyzed to identify the performance characteristics and the time taken to perform tasks using a singular mobile robot against a swarm robot environment consisting of four and ten robots, respectively, in both simulation cases. Simulations showed that the algorithm was successful in navigating obstacles for both single and swarm robot environments. A single robot was found to take up to 340% longer to arrive at the required location compared to the first robot in the experiment. Moreover, it was found that the neural network approach showed ~ 27% improvement over the modified force field algorithm when it comes to cases where more than four robots are being used.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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