Bayesian inference driven behavior network architecture for avoiding moving obstacles

Hyeun Jeong Min, Sung Bae Cho

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

This paper presents a technique for an intelligent robot to adaptively behave in unforeseen and dynamic circumstances. Since the traditional methods utilized the relatively reliable information about the environment to control intelligent robots, they were robust but could not behave adaptively in complex and dynamic world. On the contrary, behavior-based approach is suitable for generating autonomous behaviors in the environment, but it still lacks of the capabilities to infer dynamic situations for high-level behaviors. This paper proposes a 2-layer control architecture to generate adaptive behaviors, which perceive and avoid dynamic moving obstacles as well as static obstacles. The first level is to generate reflexive and autonomous behaviors with the behavior network, and the second level is to infer dynamic situation of mobile robots with Bayesian network. Experimental results with various situations have shown that the robot reaches the goal points while avoiding static or moving obstacles with the proposed architecture.

Original languageEnglish
Pages (from-to)214-221
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3682 LNAI
Publication statusPublished - 2005 Dec 1

Fingerprint

Bayesian inference
Network Architecture
Network architecture
Intelligent robots
Robot
Adaptive Behavior
Intelligent Control
Bayesian networks
Bayesian Networks
Mobile Robot
Mobile robots
Robots
Experimental Results
Architecture

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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