Nuan Shao

Papers

Total Citations

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About

Nuan Shao’s research lies at the intersection of robotic perception, visual servoing, and multi-agent control, with a focus on enabling robots to navigate and coordinate without relying on conventional sensors. In their early work, Shao developed a stereo vision obstacle detection method using SIFT feature matching, creating a depth measurement model that bypasses the need for full 3D world coordinate reconstruction—a practical step toward real-time autonomous navigation. Building on this, Shao advanced visual servoing by designing an immersion and invariance-based speed observer, allowing robots to estimate joint velocities without angular velocity sensors, a critical contribution for sensor-limited systems. More recently, Shao addressed distributed containment control in multi-agent robotic systems, proposing a position-sensorless strategy that combines binocular visual servo with feedback dissipative Hamiltonian theory. This work enables teams of robots to maintain formation around stationary leaders using only visual feedback. While citation counts for these papers remain modest (7, 5, and 2 respectively), Shao’s contributions are notable for their integration of theoretical control techniques with practical vision-based solutions, offering a pathway toward more autonomous, sensor-efficient robotic systems.