首页|Investigators at Technical University Darmstadt (TU Darmstadt) Discuss Findings in Robotics (Emergency Response Person Localization and Vital Sign Estimation Us ing a Semi-autonomous Robot Mounted Sfcw Radar)

Investigators at Technical University Darmstadt (TU Darmstadt) Discuss Findings in Robotics (Emergency Response Person Localization and Vital Sign Estimation Us ing a Semi-autonomous Robot Mounted Sfcw Radar)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Robotics. According to news originating from Darmstadt, Germany, by NewsRx corre spondents, research stated, “The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are ben eficial, especially when searching inaccessible terrains, or dangerous environme nts, such as collapsed infrastructures.” Financial support for this research came from LOEWE initiative. Our news journalists obtained a quote from the research from Technical Universit y Darmstadt (TU Darmstadt), “For search and rescue missions in degraded visual c onditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This article present s a complete signal processing chain for radar-based multi-person detection, 2D- MUSIC localization and breathing frequency estimation. The proposed method shows promising results on a challenging emergency response dataset that we collected using a semi-autonomous robot equipped with a commercially available through-wa ll radar system. The dataset is composed of 62 scenarios of various difficulty l evels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight.”

DarmstadtGermanyEuropeAutonomous R obotEmerging TechnologiesMachine LearningRobotRoboticsTechnical Univer sity Darmstadt (TU Darmstadt)

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.27)