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About

Makoto Kumon is a robotics researcher whose work spans robot audition, sound source localization, and unmanned aerial vehicles (UAVs), with particularly influential contributions to the emerging field of drone audition. His research explores how robots and autonomous systems can leverage auditory perception — integrating microphone arrays, binaural hearing principles, and advanced signal processing — to navigate and operate in complex real-world environments. Kumon's most cited contribution (53 citations) demonstrated the feasibility of embedding microphone arrays into UAVs for search-and-rescue operations, enabling outdoor sound source localization, enhancement, and robust communication in disaster scenarios. This pioneering work laid the groundwork for subsequent studies on multi-source drone audition and real-time visualization systems, addressing critical limitations of vision-based sensing under poor illumination or occlusion. Earlier in his career, Kumon made foundational advances in biologically inspired auditory robotics, developing methods for vertical sound localization using pinnae-derived spectral cues and binaural systems employing Extended Kalman Filters for tracking mobile sound sources. His research on path following control further reflects his broader interest in autonomous robot motion. With over 175 cumulative citations and active contributions across two decades, Kumon's work has meaningfully shaped how robots hear and respond to their acoustic environments, with clear humanitarian applications in disaster response.