用离散质量分布实现无需中心控制的复杂群体形状生成
Meanshift Shape Formation Control Using Discrete Mass Distribution
- 基于采样点构建离散质量分布模型，避免连续密度函数定义难题
- 通过分布式均值漂移控制使群组自适应形成目标形状并保持稳定
- 适合大规模机器人系统在动态环境中进行复杂构型重构
密度分布方法因其对群体规模变化的适应性而成为有前景的范式。然而，现有研究在实现复杂形状表示和去中心化实施方面仍面临实际挑战。为此，本文提出一种完全去中心化的、基于分布的控制策略，兼具生成复杂形状与适应群体规模变化的能力。首先，提出在一组采样点上定义的离散质量分布函数来建模群体形态；相比连续密度分布方法，该模型无需定义复杂的连续密度函数。其次，设计了去中心化的均值漂移控制律，通过反馈各采样点的质量估计值，协调群体全局分布以匹配采样点分布。所有采样点的质量估计由机器人通过所设计的质量估计算法在去中心化方式下完成，证明其可渐近收敛至真实全局值。通过大量仿真与真实实验验证，所提策略在复杂形状生成效率及群体规模变化适应性方面表现优异。
原文摘要 · Abstract (English)
The density-distribution method has recently become a promising paradigm owing to its adaptability to variations in swarm size. However, existing studies face practical challenges in achieving complex shape representation and decentralized implementation. This motivates us to develop a fully decentralized, distribution-based control strategy with the dual capability of forming complex shapes and adapting to swarm-size variations. Specifically, we first propose a discrete mass-distribution function defined over a set of sample points to model swarm formation. In contrast to the continuous density-distribution method, our model eliminates the requirement for defining continuous density functions-a task that is difficult for complex shapes. Second, we design a decentralized meanshift control law to coordinate the swarm's global distribution to fit the sample-point distribution by feeding back mass estimates. The mass estimates for all sample points are achieved by the robots in a decentralized manner via the designed mass estimator. It is shown that the mass estimates of the sample points can asymptotically converge to the true global values. To validate the proposed strategy, we conduct comprehensive simulations and real-world experiments to evaluate the efficiency of complex shape formation and adaptability to swarm-size variations.
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