无需速度感知，仅靠位置变化实现持续群体对齐
Position-Based Flocking for Persistent Alignment without Velocity Sensing
- 通过初始与当前相对位置差估算速度差异
- 引入随时间和密度变化的对齐增益，保持长期方向一致
- 适合速度测量不可靠的现实机器人集群
鸟群和鱼群的协同集体运动启发了蜂群机器人的算法设计。本文提出一种基于位置的群体运动模型，可在无速度感知条件下实现持久的速度对齐。该模型通过当前与初始相对位置的变化近似相对速度差异，并引入随时间与密度变化的对齐增益，设置非零最小阈值以维持持续对齐，从而在长时间内保持协调的集体运动。50个智能体的仿真结果表明，该模型比基于速度对齐的基线方法实现更快、更持久的方向对齐，并形成更紧凑的群体结构。实验还展示了九个真实轮式移动机器人组成的团队的应用效果。
原文摘要 · Abstract (English)
Coordinated collective motion in bird flocks and fish schools inspires algorithms for cohesive swarm robotics. This paper presents a position-based flocking model that achieves persistent velocity alignment without velocity sensing. By approximating relative velocity differences from changes between current and initial relative positions and incorporating a time- and density-dependent alignment gain with a non-zero minimum threshold to maintain persistent alignment, the model sustains coherent collective motion over extended periods. Simulations with a collective of 50 agents demonstrate that the position-based flocking model attains faster and more sustained directional alignment and results in more compact formations than a velocity-alignment-based baseline. This position-based flocking model is particularly well-suited for real-world robotic swarms, where velocity measurements are unreliable, noisy, or unavailable. Experimental results using a team of nine real wheeled mobile robots are also presented.
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