Decentralized Unlabeled Multi-Agent Navigation in Continuous Space

TL;DR

This paper introduces a decentralized multi-agent navigation method in continuous space where agents independently select goals, communicate locally, and plan paths to efficiently reach goals while avoiding collisions, outperforming baseline methods.

Contribution

The work presents a novel decentralized iterative approach for multi-agent navigation that does not rely on centralized control or predefined graphs, ensuring completeness and efficiency.

Findings

Higher success rate in reaching goals compared to baseline methods

More efficient trajectory lengths than centralized algorithms

Effective in arbitrary continuous space with local communication

Abstract

In this work, we study the problem where a group of mobile agents needs to reach a set of goal locations, but it does not matter which agent reaches a specific goal. Unlike most of the existing works on this topic that typically assume the existence of the centralized planner (or controller) and limit the agents' moves to a predefined graph of locations and transitions between them, in this work we focus on the decentralized scenarios, when each agent acts individually relying only on local observations/communications and is free to move in arbitrary direction at any time. Our iterative approach involves agents individually selecting goals, exchanging them, planning paths, and at each time step choose actions that balance between progressing along the paths and avoiding collisions. The proposed method is shown to be complete under specific assumptions on how agents progress towards their current goals, and our empirical evaluation demonstrates its superiority over a baseline decentralized navigation approach in success rate (i.e. is able to solve more problem instances under a given time limit) and a comparison with the centralized TSWAP algorithm reveals its efficiency in minimizing trajectory lengths for mission accomplishment.