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.

翻译：本研究探讨机器人听觉系统的重要性，强调其在多样化和不确定环境中运行的机器人所具备的关键作用。以机器人出租车为例，介绍了硬件设计原则，即利用外部麦克风阵列检测诸如警笛声等声音事件。本文讨论了在实现合适信噪比（SNR）方面的挑战、目标及测试方法。此外，基于概率机器人学理论，提出了一套初步的软件框架，倡导将机器人听觉整合到更广泛的感知与决策体系中。研究涉及多种模型，包括贝叶斯滤波器、部分可观测马尔可夫决策过程（POMDP）以及多智能体系统，凸显了机器人听觉可发挥的多重作用。总之，随着服务机器人的持续发展，机器人听觉研究将不断扩展，为未来发展（超越简单的声学事件分类）提供新视角与挑战。