Swarm-STL: A Framework for Motion Planning in Large-Scale, Multi-Swarm Systems

TL;DR

Swarm-STL introduces a scalable motion planning framework for large multi-agent systems using swarm abstractions and STL specifications, ensuring safety and efficiency in complex tasks.

Contribution

The paper presents a novel two-stage planning approach that reduces computational complexity by abstracting agents into swarms and planning at the swarm level.

Findings

Ensures safety and task completion in multi-swarm scenarios.

Maintains computational efficiency as the number of agents increases.

Outperforms baseline methods in large-scale multi-agent planning.

Abstract

In multi-agent systems, signal temporal logic (STL) is widely used for path planning to accomplish complex objectives with formal safety guarantees. However, as the number of agents increases, existing approaches encounter significant computational challenges. Recognizing that many complex tasks require cooperation among multiple agents, we propose swarm STL specifications to describe the collective tasks that need to be achieved by a team of agents. Next, we address the motion planning problem for all the agents in two stages. First, we abstract a group of cooperating agents as a swarm and construct a reduced-dimension state space whose dimension does not increase with the number of agents. The path planning is performed at the swarm level, ensuring the safety and swarm STL specifications are satisfied. Then, we design low-level control strategies for agents within each swarm based on the path synthesized in the first step. The trajectories of agents generated by the two-step policy ensure satisfaction of the STL specifications. We evaluate our two-stage approach in both single-swarm and multi-swarm scenarios. The results demonstrate that all tasks are completed with safety guarantees. Compared to the baseline multi-agent planning approach, our method maintains computational efficiency as the number of agents increases, since the computational time scales with the number of swarms rather than the number of agents.