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About

Jun Yoneyama is a robotics researcher dedicated to advancing autonomous systems for disaster response and healthcare. His primary research areas include real-time obstacle detection, autonomous mobile robotics, and sensor-based human posture monitoring. Yoneyama’s major contribution is the development of a monocular camera and cross-line laser system that enables robots to measure obstacle distances in real time, independent of ambient brightness—a critical capability for navigating dark, unpredictable disaster zones. His most cited work, "Real-time obstacle detection in a darkroom using a monocular camera and a line laser" (2022, 10 citations), demonstrates a practical solution for early victim detection, directly addressing the urgent need for faster rescue operations. Expanding his impact beyond robotics, Yoneyama has also applied sensor technology to healthcare, developing a twisting posture detection method using triaxial accelerometers to prevent occupational low back pain in nurses and caregivers. This work highlights his commitment to solving real-world problems, from improving disaster robot reliability to enhancing workplace safety in understaffed medical environments. Through his innovative, low-cost sensing approaches, Yoneyama continues to bridge the gap between laboratory research and life-saving applications.