RSC: Decentralized Rigid Formation Flocking for Large-Scale Swarms via Hybrid Predictive Control and Online Reconfiguration

2026-06-02

AI Summary

This work addresses the challenge of simultaneously achieving rigid formation maintenance, dynamic obstacle avoidance, and target tracking in large-scale multi-agent systems operating under limited local sensing and communication. Existing approaches often suffer from local optima or control oscillations. To overcome these limitations, the authors propose the RSC framework, which integrates finite-horizon model predictive control with an artificial potential field–based reactive safety strategy, thereby combining long-term planning with immediate collision avoidance in a hybrid architecture. Additionally, an online leader-follower role-switching mechanism enables rapid, uninterrupted formation reconfiguration during task execution. Evaluated in dense obstacle environments with 25 drones, the method achieves an 83% success rate—defined as maintaining inter-agent edge-length errors below 10% without collisions—significantly outperforming existing heuristic and learning-based baselines, which achieve less than 5%.

Abstract

Decentralized rigid formation flocking requires a swarm of autonomous agents to maintain a predetermined geometric configuration while moving, relying solely on local sensing and communication. However, existing decentralized control methods struggle to maintain strict inter-agent distance constraints in cluttered environments, often suffering from local minima deadlocks, high frequency control oscillations, or limited flexibility during obstacle navigation, resulting in low success rate. To address these limitations, we propose Rigid Swarm Control (RSC), a decentralized control framework for large-scale rigid formation flocking. To escape local minima via robust long-term planning while ensuring short-term safety, RSC integrates finite-horizon trajectory predictions with a reactive artificial potential field (APF) safety controller within a hybrid architecture. Furthermore, to accelerate formation reassembly after obstacle traversal without interrupting task execution, RSC introduces an online leader-follower reconfiguration mechanism based on stable role exchange. Extensive evaluations in challenging cluttered environments with 25 UAVs demonstrate that RSC reliably unifies rigid formation maintenance, obstacle avoidance, and target tracking. Under strict success criteria - collision-free operation with a maximum relative edge-length error below 10%, RSC achieves an 83% success rate, significantly outperforming existing heuristic and learning-based baselines that fall below 5%.

Problem

• decentralized rigid formation flocking
• local minima
• obstacle avoidance
• large-scale swarms
• inter-agent distance constraints

Innovation

• decentralized control
• rigid formation flocking
• hybrid predictive control
• online reconfiguration
• artificial potential field