Generation of Fuzzy Rules and Learning Algorithms for Cooperative Behavior of Autonomouse Mobile Robots(AMRs)

2005 ◽  
pp. 1015-1024 ◽  
Author(s):  
Jang-Hyun Kim ◽  
Jin-Bae Park ◽  
Hyun-Seok Yang ◽  
Young-Pil Park
Keyword(s):  
Mobile Robots ◽  
Cooperative Behavior ◽  
Learning Algorithms ◽  
Fuzzy Rules

scholarly journals Realization of Cooperative Behavior for Multiple Mobile Robots on Catching Problem

2004 ◽  
Vol 124 (2) ◽  
pp. 502-508 ◽  
Author(s):  
Manabu Kariya ◽  
Takuya Kamano ◽  
Takashi Yasuno ◽  
Takayuki Suzuki ◽  
Hironobu Harada ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Cooperative Behavior ◽  
Multiple Mobile Robots

scholarly journals Iterative SARSA: The Modified SARSA Algorithm for Finding the Optimal Path

2020 ◽  
Vol 8 (6) ◽  
pp. 4333-4338
Keyword(s):  
Reinforcement Learning ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Optimal Path ◽  
Autonomous Mobile Robots ◽  
Current Standard ◽  
Q Learning ◽  
Better Than

This paper presents a thorough comparative analysis of various reinforcement learning algorithms used by autonomous mobile robots for optimal path finding and, we propose a new algorithm called Iterative SARSA for the same. The main objective of the paper is to differentiate between the Q-learning and SARSA, and modify the latter. These algorithms use either the on-policy or off-policy methods of reinforcement learning. For the on-policy method, we have used the SARSA algorithm and for the off-policy method, the Q-learning algorithm has been used. These algorithms also have an impacting effect on finding the shortest path possible for the robot. Based on the results obtained, we have concluded how our algorithm is better than the current standard reinforcement learning algorithms

scholarly journals A line follower robot implementation using Lego's Mindstorms Kit and Q-Learning

2012 ◽  
Vol 22 ◽  
pp. 113-118 ◽  
Author(s):  
Víctor Ricardo Cruz-Álvarez ◽  
Enrique Hidalgo-Peña ◽  
Hector-Gabriel Acosta-Mesa
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Programming Environment ◽  
Q Learning

A common problem working with mobile robots is that programming phase could be a long, expensive and heavy process for programmers. The reinforcement learning algorithms offer one of the most general frameworks in learning subjects. This work presents an approach using the Q-Learning algorithm on a Lego robot in order for it to learn by itself how to follow a blackline drawn down on a white surface, using Matlab [5] as programming environment.

Motion Control of Family Mobile Robots with Hierarchical Cooperative Behavior

1995 ◽  
Vol 28 (11) ◽  
pp. 337-341
Author(s):  
Hirotugu Nagami ◽  
Hisafumi Miyamoto ◽  
Susumu Sakano
Keyword(s):  
Mobile Robots ◽  
Motion Control ◽  
Cooperative Behavior
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