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

Jie Song, Yang Bai, Naoki Wakamiya

TL;DR

Simulation results demonstrate that the proposed framework can simultaneously transport multiple cargos of different sizes in a coordinated and collision-free manner.

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.

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

TL;DR

Simulation results demonstrate that the proposed framework can simultaneously transport multiple cargos of different sizes in a coordinated and collision-free manner.

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.
Paper Structure (9 sections, 10 equations, 2 figures)

This paper contains 9 sections, 10 equations, 2 figures.

Table of Contents

  1. INTRODUCTION
  2. Preliminary
  3. Cooperative transportation strategy and controller design
  4. Problem statement
  5. Control strategy design
  6. Object detection and caging formation
  7. CBF-based controller design
  8. Simulation results
  9. Conclusion

Figures (2)

  • Figure 1: Snapshots of the simulation process at different time steps: (a) $t = 0\,\mathrm{s}$, (b) $t = 31\,\mathrm{s}$, (c) $t = 82\,\mathrm{s}$, and (d) $t = 124\,\mathrm{s}$. In each snapshot, the left panel illustrates the spatial configuration of the agents and objects in the workspace, where black dots denote the agents and blue circles indicate their local detection ranges. The orange circles represent target objects with different sizes. The right panel shows the corresponding object-induced density function $\phi(\boldsymbol{q}, t)$, which is dynamically constructed based on agents’ local detections and guides the cooperative enclosure behavior.
  • Figure 2: Agent trajectories during the object detection and transportation process.