Daniel A. Bender

Fraunhofer Institute for Communication, Information Processing and Ergonomics

Papers

Total Citations

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About

Daniel A. Bender is a robotics researcher specializing in autonomous navigation, particularly for aerial platforms operating in GPS-denied environments. His work addresses critical limitations in drone navigation by developing map-based homing strategies that reduce reliance on satellite signals. Bender's most cited paper, "Map-based drone homing using shortcuts" (2017), proposes a novel approach that enables drones to navigate efficiently using visual cues and precomputed shortcuts, achieving robust localization without continuous GPS input. This contribution is foundational for applications in search-and-rescue, infrastructure inspection, and military operations where GPS signals may be unreliable or jammed. With 7 citations, this work has influenced subsequent studies in visual navigation and path planning for unmanned aerial vehicles. Bender's research bridges the gap between theoretical mapping algorithms and practical deployment, emphasizing real-time performance and computational efficiency. His achievements highlight the growing importance of alternative navigation methods in robotics, positioning him as a key contributor to the advancement of autonomous drone systems.