Multi-Agent Continuous Transportation with Online Balanced Partitioning

TL;DR

This paper presents new algorithms for multi-agent continuous transportation tasks that minimize communication and interference, outperforming existing methods without relying on reconnaissance agents.

Contribution

The paper introduces a novel online partitioning-transportation algorithm with information gathering, and hybrid approaches that reduce communication needs in multi-agent systems.

Findings

Algorithms outperform existing methods in simulations

Effective without inter-agent communication

No reconnaissance agent needed

Abstract

We introduce the concept of continuous transportation task to the context of multi-agent systems. A continuous transportation task is one in which a multi-agent team visits a number of fixed locations, picks up objects, and delivers them to a final destination. The goal is to maximize the rate of transportation while the objects are replenished over time. Examples of problems that need continuous transportation are foraging, area sweeping, and first/last mile problem. Previous approaches typically neglect the interference and are highly dependent on communications among agents. Some also incorporate an additional reconnaissance agent to gather information. In this paper, we present a hybrid of centralized and distributed approaches that minimize the interference and communications in the multi-agent team without the need for a reconnaissance agent. We contribute two partitioning-transportation algorithms inspired by existing algorithms, and contribute one novel online partitioning-transportation algorithm with information gathering in the multi-agent team. Our algorithms have been implemented and tested extensively in the simulation. The results presented in this paper demonstrate the effectiveness of our algorithms that outperform the existing algorithms, even without any communications between the agents and without the presence of a reconnaissance agent.