Scholarlink

<p>In scaled-down sand table multi-agent formation experiments, existing control strategies often focus on path accuracy or formation stability, neglecting energy consumption constraints. This leads to a lack of energy awareness and inefficient energy utilization, and limited endurance under high-precision control. To address this issue, this paper constructs a continuous action decision layer driven by Eagle Strike SAC (Soft Actor-Critic) to achieve energy-aware control of speed and turning angle. It integrates the ant colony pheromone mechanism and GNN (Graph Neural Network) to complete group situational awareness modeling, and uses Wolf Pack FedAvg (Federated Averaging) to achieve distributed policy co-evolution, forming a cross-layer closed-loop architecture of “decision-perception-aggregation.” Simultaneously, a sand table physical energy consumption mapping model is introduced to embed actual power consumption into the learning process, improving formation stability while reducing energy consumption. Experimental results based on the Robotarium dataset demonstrate that the proposed method reduces energy consumption per unit distance by approximately 39% compared with MPC, while achieving a steady-state formation error of 0.03–0.05&#xa0;m and a convergence time of 7.2&#xa0;s. Compared with MADDPG and SAC baselines, the framework also shows consistent improvements in energy stability and trajectory smoothness. In addition, communication overhead is significantly reduced through selective parameter updates, without sacrificing convergence performance. These results validate that the proposed approach demonstrates a balanced and scalable solution for energy-aware multi-agent formation control, with improved energy efficiency under the evaluated settings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Energy-aware multi-agent sand-table formation control via cross-layer SAC–GNN and federated learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月20日

查看全文
  • Ruting Li,
  • Wenni Xiao,
  • Yan Sun,
  • Wei Jiang

摘要

In scaled-down sand table multi-agent formation experiments, existing control strategies often focus on path accuracy or formation stability, neglecting energy consumption constraints. This leads to a lack of energy awareness and inefficient energy utilization, and limited endurance under high-precision control. To address this issue, this paper constructs a continuous action decision layer driven by Eagle Strike SAC (Soft Actor-Critic) to achieve energy-aware control of speed and turning angle. It integrates the ant colony pheromone mechanism and GNN (Graph Neural Network) to complete group situational awareness modeling, and uses Wolf Pack FedAvg (Federated Averaging) to achieve distributed policy co-evolution, forming a cross-layer closed-loop architecture of “decision-perception-aggregation.” Simultaneously, a sand table physical energy consumption mapping model is introduced to embed actual power consumption into the learning process, improving formation stability while reducing energy consumption. Experimental results based on the Robotarium dataset demonstrate that the proposed method reduces energy consumption per unit distance by approximately 39% compared with MPC, while achieving a steady-state formation error of 0.03–0.05 m and a convergence time of 7.2 s. Compared with MADDPG and SAC baselines, the framework also shows consistent improvements in energy stability and trajectory smoothness. In addition, communication overhead is significantly reduced through selective parameter updates, without sacrificing convergence performance. These results validate that the proposed approach demonstrates a balanced and scalable solution for energy-aware multi-agent formation control, with improved energy efficiency under the evaluated settings.