Environment prediction for a mobile robot in a dynamic environment

Author

Chang, Charles C. ; Song, Kai-Tai

Author_Institution

Dept. of Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan

Volume

13

Issue

6

fYear

1997

fDate

12/1/1997 12:00:00 AM

Firstpage

862

Lastpage

872

Abstract

The problem of navigating a mobile robot among moving obstacles is usually solved on the condition of knowing the velocity of obstacles. However, it is difficult to provide such information to a robot in real time. In this paper, we present an environment predictor that provides an estimate of future environment configuration by fusing multisensor data in real time. The predictor is implemented by an artificial neural network (ANN) trained using a relative-error-backpropagation (REBP) algorithm. The REBP algorithm enables the ANN to provide output data with a minimum relative error, which is better than conventional backpropagation (BP) algorithms in this prediction application. The mobile robot can, therefore, respond to anticipated changes in the environment. The performance is verified by prediction simulation and navigation experiments

Keywords

backpropagation; computerised navigation; mobile robots; neural nets; path planning; real-time systems; sensor fusion; REBP algorithm; artificial neural network; dynamic environment; environment prediction; minimum relative error; mobile robot; moving obstacles; navigation; real-time multisensor data fusion; relative-error-backpropagation; Artificial neural networks; Backpropagation algorithms; Control engineering; Mobile robots; Motion planning; Navigation; Orbital robotics; Robot sensing systems; Sensor systems; Trajectory;

fLanguage

English

Journal_Title

Robotics and Automation, IEEE Transactions on

Publisher

ieee

ISSN

1042-296X

Type

jour

DOI

10.1109/70.650165

Filename

650165