机器人分队行进在密集人流中更少干扰，但需根据人流方向自适应切换策略。
On the Benefits of Robot Platooning for Navigating Crowded Environments
- 分队行进（platooning）比盲目抢行（greedy）更少干扰人群
- 分队在静止和逆向人流中表现更好，而顺向人流中抢行更优
- 提出自适应策略，可自动切换模式，全场景表现更优
本文研究机器人组如何有效穿越人群。通过模拟最多200台机器人的三种场景——静止人群、逆向人流、垂直人流——对比分队行进与无约束贪婪策略的性能及其对人群的干扰程度。结果显示：在静止和逆向人流中，分队策略干扰更小且在高密度环境下更高效；而在垂直人流中，贪婪策略在效率与干扰控制上均优于分队策略。为此，本文提出一种可动态切换分队与贪婪行为的自适应策略，在所有场景中均融合两者优势，实现最优表现。
原文摘要 · Abstract (English)
This paper studies how groups of robots can effectively navigate through a crowd of agents. It quantifies the performance of platooning and less constrained, greedy strategies, and the extent to which these strategies disrupt the crowd agents. Three scenarios are considered: (i) passive crowds, (ii) counter-flow crowds, and (iii) perpendicular-flow crowds. Through simulations consisting of up to 200 robots, we show that for navigating passive and counter-flow crowds, the platooning strategy is less disruptive and more effective in dense crowds than the greedy strategy, whereas for navigating perpendicular-flow crowds, the greedy strategy outperforms the platooning strategy in either aspect. Moreover, we propose an adaptive strategy that can switch between platooning and greedy behavioral states, and demonstrate that it combines the strengths of both strategies in all the scenarios considered.
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