ALAN-P: Dynamic action pruning for efficient navigation in complex environments

Authors

Keywords:

multi agent navigation, multi agent coordination, multi agent learning, simulation

Abstract

Multi-agent navigation consists on efficiently moving a set of agents from start to goal locations. This is a challenging task since most environments contain static and dynamic obstacles that can significantly restrict the movement of an agent. While existing methods, such as ALAN, can overcome some of these limitations by increasing the action space of the agents, their behavior can be suboptimal in many of the situations that agents can find themselves into. In this work, we propose ALAN-P, a multi agent local navigation method based on the ALAN framework, which improves the agent's behavior by dynamically adapting the action space to the agent's local conditions. The results of our experiments show that the proposed ALAN-P can lead to significant performance improvements over ALAN in a variety of challenging environments.

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Author Biographies

Julio Godoy, Department of Computer Science, Universidad de Concepcion, Chile

obtuvo su doctorado en Computación en la Universidad de Minnesota, USA. Actualmente es profesor asistente en la Universidad de Concepción, Chile. Sus intereses de investigación están en la intersección de inteligencia artificial distribuida y robótica.

Fernando Gutierrez, AIDA SpA

obtuvo su doctorado en Computación en la Universidad de Oregon, USA. A través de su cargo en AIDA, trabaja en la integración de técnicas de Inteligencia Artificial a procesos industriales. Sus áreas de interés son integración de representación de conocimiento en sistemas multi-agentes y Web Semántica.

Joaquin Soto, Department of Computer Science, Universidad de Concepción, Chile

obtuvo el título de Ingeniero civil informático en la Universidad de Concepción. Sus áreas de interés son la ingeniería de software y el desarrollo de proyectos en general.

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Published

2022-05-03

How to Cite

Godoy, J., Gutierrez, F., & Soto, J. (2022). ALAN-P: Dynamic action pruning for efficient navigation in complex environments. IEEE Latin America Transactions, 100(XXX). Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6346