Cooperative Transportation Without Prior Object Knowledge via Adaptive Self-Allocation and Coordination
This addresses the challenge of autonomous cargo transport in multi-agent systems, offering a solution that is incremental by building on existing coordination methods like CVT and CBF.
The paper tackles the problem of multi-agent cooperative transportation without prior knowledge of cargo properties by introducing a framework where agents use local sensing and attraction fields to self-organize and allocate teams adaptively, achieving coordinated and collision-free transport of multiple cargos of varying sizes in simulations.
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.