基于位置的持久对齐编队无需速度感知
Position-Based Flocking for Persistent Alignment without Velocity Sensing
AI总结：
本文提出一种无需速度感知的基于位置的编队模型，通过近似相对速度差异和时间密度依赖的对齐增益，实现持久速度对齐，适用于现实世界机器人群体。
摘要：
鸟群和鱼群的协调集体运动启发了具有凝聚力的群体机器人算法。本文提出了一种基于位置的编队模型，实现了无需速度感知的持久速度对齐。通过从当前和初始相对位置的变化近似相对速度差异，并结合时间依赖性和密度依赖的对齐增益，以及非零最小阈值以维持持久对齐，该模型在长时间内维持了协调的集体运动。对50个代理的模拟显示，基于位置的编队模型在方向对齐方面比基于速度对齐的基线更快且更持久，并产生更紧凑的编队。这种基于位置的编队模型特别适用于现实世界的机器人群体，其中速度测量不可靠、嘈杂或不可用。还展示了使用九个真实轮式移动机器人的实验结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
