发表机构
New Jersey Institute of Technology; University of Michigan（新泽西理工学院; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
受鸟类启发，提出利用调频啁啾信号的频率间隙定义反射椭圆，结合累积滤波器实现协作多智能体全向回声定位，仿真与实地实验验证了低成本环境映射的可行性。
AI 中文摘要
受鸟类飞行和鸣叫的启发，我们提出了一种方法，使协作智能体能够通过让一个静止的源智能体发射调频啁啾信号，同时一个或多个监听智能体在运动过程中观察直接信号和反射信号，从而有效地识别其环境中的障碍物。我们提出了一种新颖的映射方法，利用直接啁啾信号与反射啁啾信号之间的“频率间隙”来定义候选反射椭圆，然后将这些椭圆输入二维累积滤波器，以识别具有最高潜在反射密度的位置。我们首先通过一个高效、定制的音频模拟器获得的仿真结果来展示这项工作，考察了障碍物数量、采样率和路径曲率对单监听器场景下准确识别环境障碍物的影响。然后，我们研究了双监听器配置的不同协作运动策略。最后，我们通过在户外公园环境中进行的实地实验验证了该方法的实际可行性，展示了使用现成硬件识别频率间隙的能力。我们的结果为群体机器人系统中低成本环境映射方法奠定了基础。
英文摘要
Inspired by the flight and song of birds, we propose an approach that enables cooperating agents to effectively identify obstacles in their environment by having a stationary source agent emit a frequency-modulated chirp while one or more listener agents observe the direct and reflected signals while in motion. We present a novel mapping approach that exploits the ``frequency gap'' between direct and reflected chirp signals to define candidate reflection ellipses, which are then fed into a 2D accumulation filter to identify locations with the highest density of potential reflections. We demonstrate this work first with simulation results derived from an efficient, bespoke audio simulator, examining the effect of obstacle count, sampling rate, and path curvature on the ability to accurately identify environmental obstacles for a one-listener scenario. We then examine different cooperative motion strategies for two-listener configurations. Finally, we validate the real-world viability of this approach through field experiments in an outdoor park setting to demonstrate the ability to identify frequency gaps using off-the-shelf hardware. Our results provide a foundation for a low-cost approach to environmental mapping in swarm robotic systems.
Comments8 pages. Accepted to IROS 2026
