为机器人打造耳朵：自主时代的机器听觉
Building Ears for Robots: Machine Hearing in the Age of Autonomy
AI总结：
本文以 robotaxi 外部麦克风阵列为例探讨机器人听觉的硬件设计与 SNR 测试，并提出基于概率机器人学、Bayes 滤波和 POMDP 的软件框架，将听觉纳入感知与决策。
摘要：
本研究探讨了机器人听觉系统的重要意义，强调其对于在多样且不确定环境中运行的机器人的重要性。文中以 robotaxi（无人驾驶出租车）为例介绍硬件设计原则，即采用外部麦克风阵列来探测警笛等声音事件。文章讨论了相关挑战、目标和测试方法，重点在于获得合适的信噪比（SNR）。此外，本文提出了一个植根于概率机器人学理论的初步软件框架，主张将机器人听觉整合到更广泛的感知与决策情境中。文中讨论了多种模型，包括 Bayes 滤波器、部分可观测 Markov 决策过程（POMDP）以及多智能体系统，突显机器人听觉能够发挥的多方面作用。总之，随着服务机器人持续发展，机器人听觉研究将会扩展，为超越简单声音事件分类的未来发展提供新的视角与挑战。
英文摘要：
This study explores the significance of robot hearing systems, emphasizing their importance for robots operating in diverse and uncertain environments. It introduces the hardware design principles using robotaxis as an example, where exterior microphone arrays are employed to detect sound events such as sirens. The challenges, goals, and test methods are discussed, focusing on achieving a suitable signal-to-noise ratio (SNR). Additionally, it presents a preliminary software framework rooted in probabilistic robotics theory, advocating for the integration of robot hearing into the broader context of perception and decision-making. It discusses various models, including Bayes filters, partially observable Markov decision processes (POMDP), and multiagent systems, highlighting the multifaceted roles that robot hearing can play. In conclusion, as service robots continue to evolve, robot hearing research will expand, offering new perspectives and challenges for future development beyond simple sound event classification.
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。
