Chi‐Chia Sun

About

Chi-Chia Sun is a researcher whose work bridges algorithmic theory and practical robotics, with a focus on computational geometry and deep learning for autonomous navigation. His most cited paper, "An O(n log n) Shortest Path Algorithm Based on Delaunay Triangulation" (2013, 49 citations), makes a significant contribution to path planning by offering an efficient solution for Euclidean and λ-geometry environments with obstacles, advancing the roadmap approach to the shortest path problem. In recent years, Sun has shifted toward hardware-accelerated robotics, developing novel methods for floor region segmentation and estimation in unmanned ground vehicles (UGVs). His 2020 work on a Binary Fully Convolutional Neural Network (B-FCN) optimized via the Taguchi method (5 citations) enables precise floor detection in complex indoor settings, while his 2019 algorithm integrating deep learning with fuzzy integrals (2 citations) and a 2022 fast estimation technique (2 citations) further enhance UGV perception. Sun’s contributions are notable for combining theoretical rigor with embedded system acceleration, as seen in his SoC FPGA implementations, making his work valuable for students and researchers in robotics, computer vision, and efficient algorithm design.

Research Focus

Key Achievements

Top Papers

1. 1
   An $\bm{O(n\log n)}$ Shortest Path Algorithm Based on Delaunay Triangulation

   49 citations · 2013
2. 2
   SoC FPGA Accelerated Sub-Optimized Binary Fully Convolutional Neural Network for Robotic Floor Region Segmentation

   5 citations · 2020
3. 3
   A New Floor Region Estimation Algorithm Based on Deep Learning Networks with Improved Fuzzy Integrals for UGV Robots

   2 citations · 2019
4. 4
   A new fast estimating floor region based on image segmentation for smart rovers

   2 citations · 2022

Key Collaborators

Contact & Links