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This paper introduces FLIP, a distributed formation planning algorithm for large-scale swarms that reframes the Optimal Formation Position Sequence (OFPS) calculation as a spatiotemporal Point Cloud Registration (PCR) problem. By using PCR with outlier rejection, FLIP enables each agent to distributively compute its OFPS based on the positions of other agents, improving resilience to suboptimal trajectories and failed agents. Simulations with 120 drones demonstrate FLIP's efficiency and resilience compared to state-of-the-art methods.
Achieve resilient, real-time formation planning for massive drone swarms by cleverly recasting the problem as point cloud registration.
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.
