Paper Info
Title
Teleoperation system for real world robots-adaptive robot navigation based on sensor fusion.
Abstract
In this paper, we propose a teleoperation system with an autonomous robot, which is able to solve tasks even without a large load for the operator, and the system. Most teleoperation systems require skilled operators and expensive interfaces to solve tasks because they assume that the operator controls a robot completely. For these problems, we propose a teleoperation system, which consists of an operation system and an autonomous robot. The operation system has a man-machine interface and allows user to specify the working space and the tasks to be done. The autonomous robot follows the instruction from the operation system to solve the certain tasks. This paper focuses on navigation problems of the autonomous robot as an essential part of the proposed system. Namely, the autonomous robot should keep on the instructed paths in the real world to achieve a goal of the tasks. Our approach is based on a sensor fusion method based on the learning schemes, that is, the Self-Organizing Map (SOM) and the reinforcement learning. These learning schemes allow the system to be able to solve the tasks in the unreliable environment such as an outdoor. The computational simulations reveal effectiveness and robustness of the proposed method in the navigation problem.
Year
DOI
Venue
2000
10.1109/PADSW.2000.884672
ICPADS Workshops
Keywords
DocType
ISBN
navigation problem,reinforcement learning,adaptive robot navigation,autonomous robot,self-organizing map,real world robots,sensor fusion method,proposed system,teleoperation system,operation system,skilled operator,sensor fusion,robustness,telerobotics,control systems,self organizing map,robot control,motion planning,man machine interface,user interfaces,computational modeling,computer simulation,learning artificial intelligence,mobile robots,operating system
Conference
0-7695-0571-6
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Yoshikazu Arai15118.63
Jun Hakura28515.06