Multi-agent navigation using path-based vector fields

Authors:
Tristan Behrens;Randolf Schärfig;Tim Winkler
Affiliations:
Department of Informatics, Clausthal University of Technology, Clausthal, Germany;Department of Informatics, Clausthal University of Technology, Clausthal, Germany;Department of Informatics, Clausthal University of Technology, Clausthal, Germany
Venue:
MATES'09 Proceedings of the 7th German conference on Multiagent system technologies
Year:
2009

Citing 10
Cited 0

An Behavior-based Robotics

An Behavior-based Robotics
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
Field-Based Motion Coordination in Quake 3 Arena

AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Programming Multi-Agent Systems: Languages, Frameworks, Techniques and Tools (Multiagent Systems, Artificial Societies, and Simulated Organizations)

Programming Multi-Agent Systems: Languages, Frameworks, Techniques and Tools (Multiagent Systems, Artificial Societies, and Simulated Organizations)
Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
Fundamentals of Computer Graphics, Second Ed.

Fundamentals of Computer Graphics, Second Ed.
2APL: a practical agent programming language

Autonomous Agents and Multi-Agent Systems
Using multi-agent potential fields in real-time strategy games

Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
A field-based versus a protocol-based approach for adaptive task assignment

Autonomous Agents and Multi-Agent Systems
Multi-Agent Programming: Languages, Tools and Applications

Multi-Agent Programming: Languages, Tools and Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

We present an approach to multi-agent navigation, that is based on generating potential-fields from A*-paths. We will introduce and compare two algorithms: 1) a geometrical algorithm that is based on quads, and 2) an images-based algorithm. We show empirically that the images-based algorithm is more memory-consuming, but has better performance.