FLIP: Real-Time and Resilient Formation Planning for Large-Scale DIstributed Swarms via Point Cloud Registration

2026-05-28

AI Summary

This work addresses the limitations of existing large-scale formation planning methods, which either suffer from performance degradation due to oversimplified formation representations or incur high computational complexity from full inter-agent coordination. The authors formulate the optimal formation waypoint sequence generation problem as a spatiotemporal point cloud registration task with outlier rejection—a novel formulation introduced for the first time to large-scale multi-agent formation planning. Each agent independently generates its trajectory by performing distributed registration between the current and target formation structures, while collaboratively optimizing paths and effectively isolating malfunctioning agents and suboptimal trajectories. In simulations involving 120 drones, the proposed method demonstrates superior performance over state-of-the-art approaches in terms of planning quality, robustness, and scalability.

Abstract

Traditional large-scale formation planning either oversimplify the formation representation which leads to poor performance, or they employ complete collaborative relationships, which results in excessive computational load. To achieve high-performance and large-scale formation planning, we transform the Optimal Formation Position Sequence \cite{c1} (OFPS) calculation problem into a spatiotemporal Point Cloud Registration (PCR) problem. Each agent derives its OFPS by distributively computing the matching result between current positions and the desired formation positions of all other agents. Then each agent optimizes the cooperative formation trajectory by using OFPS. We leverage the PCR method with outlier rejection to rapidly perform large-scale formation position registration. This prevents suboptimal trajectories and failed agents from propagating through the cooperative network and affecting more agents. Consequently, we uniformly achieve resilient, efficient, and distributed trajectory planning for large-scale swarms. The effectiveness and the superiority of the proposed method are demonstrated through large-scale simulations of 120-drone formation, and rigorous benchmarking against state-of-the-art (SOTA) methods.

Problem

• formation planning
• large-scale swarms
• point cloud registration
• distributed coordination
• computational complexity

Innovation

• Point Cloud Registration
• Formation Planning
• Distributed Swarm
• Outlier Rejection
• Optimal Formation Position Sequence