Dan Shao

Shenyang University of Technology, Austrian Institute of Technology

Papers

Total Citations

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About

Dan Shao’s research sits at the intersection of robotics, decision science, and visual surveillance, with a focus on enabling autonomous systems to perceive and interact with their environments more intelligently. His most notable contribution is in image-based control of mobile robots, where he developed a novel switching control scheme that allows nonholonomic robots to regulate their position using only a single onboard camera—eliminating the need for complex pose reconstruction or homography decomposition. This work, published in 2021, has already garnered 3 citations and represents a significant step toward simpler, more robust visual servoing for field robotics. Earlier in his career, Shao contributed to multiple attribute decision making, proposing an integration approach that combines subjective expert preferences—such as preference orderings, utility values, and fuzzy relations—into a unified framework for evaluating alternatives like robotic systems. His 2008 paper on this topic also holds 3 citations. Shao has also worked on real-time human detection for visual surveillance, presenting a fast contour template matching method at the AVSS 2011 demo session. Across these diverse areas, Shao’s work demonstrates a consistent drive to bridge theoretical algorithms with practical, real-world deployment challenges.