Cooperative Transportation Without Prior Object Knowledge via Adaptive Self-Allocation and Coordination

TL;DR

This paper introduces a decentralized multi-agent transportation method that autonomously detects cargos, recruits agents, and organizes into balanced teams without prior cargo knowledge, using adaptive attraction fields and safety mechanisms.

Contribution

It presents a novel framework combining attraction fields, CVT, and CBFs for autonomous, balanced, and collision-free multi-cargo transportation without prior object information.

Findings

Successfully transports multiple cargos of different sizes simultaneously.

Agents self-organize into balanced formations around cargos.

Framework ensures collision-free and symmetric agent distribution.

Abstract

This work proposes a novel cooperative transportation framework for multi-agent systems that does not require any prior knowledge of cargo locations or sizes. Each agent relies on local sensing to detect cargos, recruit nearby agents, and autonomously form a transportation team with an appropriate size. The core idea is that once an agent detects a cargo within its sensing range, it generates an attraction field represented by a density function, which pulls neighboring agents toward the cargo. When multiple cargos are present, the attraction fields generated by different agents are adaptively weighted and combined with Centroidal Voronoi Tessellation (CVT), enabling agents to self-organize into balanced formations while automatically allocating more agents to larger cargos. To prevent agents from clustering on one side of a large cargo, a Control Barrier Function (CBF)-based mechanism is introduced to enforce safe inter-agent distances and promote a uniform, symmetric distribution of agents around each cargo, which is essential for stable transportation. Simulation results demonstrate that the proposed framework can simultaneously transport multiple cargos of different sizes in a coordinated and collision-free manner.