发表机构
Sun Yat-sen University（中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于GRF的分布式随机最优控制框架，通过贝叶斯推理和密度引导实现图案化蜂群的安全导航与自愈重构，并经仿真与实物实验验证。
AI 中文摘要
尽管图案化蜂群在多种应用中展现出巨大潜力，但其在几何控制、自组织以及通过动态环境的安全导航方面仍面临多方面挑战。本文提出了一种基于GRF的随机最优控制框架，在统一的概率架构内解决这些挑战。通过将GRF扩展到时间域，所提出的框架将集体协调视为贝叶斯推理任务，使蜂群能够适应环境不确定性、满足非凸约束，并协调不同平台间的异构动力学。我们开发了一个不确定性与安全感知的碰撞避免模块，用于在随机障碍物运动存在的情况下进行导航。采用无迹变换来传播动态障碍物和邻近智能体的状态不确定性，从而为碰撞避免提供合理的置信界限。此外，引入了密度引导的图案控制，将几何图案编码为隐式密度场。这种表示将图案规范与显式的智能体到目标分配解耦，从而以分布式方式促进内在的自愈和弹性重构。所提出的框架通过跨多种场景的蒙特卡洛模拟进行了广泛评估。其模型无关的特性在四旋翼和固定翼无人机蜂群上均得到了验证，突显了其在不同动力学平台间的泛化能力。最后，通过包含15架四旋翼的室内实验和涉及4架定制自主四旋翼的室外部署，验证了所提方法的有效性和鲁棒性。这些实验证实了所提出的框架在真实环境中维持可靠几何图案转换和安全感知导航的能力。
英文摘要
While offering significant promise for diverse applications, pattern-oriented swarms encounter multifaceted challenges in geometric control, self-organization, and safe navigation through dynamic environments. In this paper, we present a GRF-based stochastic optimal control framework to address these challenges within a unified probabilistic architecture. By extending the GRF into the temporal domain, the proposed framework casts collective coordination as a Bayesian inference task, enabling swarms to accommodate environmental uncertainty, satisfy non-convex constraints, and reconcile heterogeneous dynamics across diverse platforms. We develop an uncertainty- and safety-aware collision avoidance module for navigation in the presence of stochastic obstacle motion. The unscented transform is employed to propagate state uncertainty for both dynamic obstacles and neighboring agents, yielding principled confidence bounds for collision avoidance. In addition, density-guided pattern control is introduced, which encodes geometric patterns as implicit density fields. This representation decouples pattern specification from explicit agent-to-target assignments, thereby facilitating intrinsic self-healing and elastic reconfiguration in a distributed manner. The proposed framework is extensively evaluated through Monte Carlo simulations across diverse scenarios. Its model-agnostic nature is demonstrated on both quadrotor and fixed-wing UAV swarms, highlighting its generalizability across platforms with heterogeneous dynamics. Finally, the efficacy and robustness of the proposed method are validated through indoor experiments with a 15-quadrotor swarm and outdoor deployments involving 4 custom-built autonomous quadrotors. These experiments substantiate the proposed framework's capacity to maintain reliable geometric pattern transitions and safety-aware navigation within real-world environments.
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