Pei Ling Ng
Papers
1
Total Citations
6
H-Index
1
About
Pei Ling Ng is a researcher in robotics and artificial intelligence, with a focus on autonomous navigation and path-planning algorithms. Her most notable contribution is the development and evaluation of a recursive backtracking depth-first search (DFS) algorithm for self-learning path-finding robots operating in unknown search spaces. This work, published in 2021 and garnering 6 citations, addresses a critical challenge in mobile robotics: enabling robots to efficiently explore and navigate unfamiliar environments without prior maps. Ng’s algorithm demonstrates how recursive backtracking can be adapted for real-time, adaptive learning, allowing robots to iteratively refine their paths through trial and error. This approach has implications for applications such as search-and-rescue operations, warehouse automation, and autonomous exploration. While her citation count is modest, the work stands out for its practical, hands-on methodology—combining theoretical algorithm design with physical robot implementation. Ng’s research bridges the gap between classical search algorithms and modern autonomous systems, offering a scalable solution for robots that must learn from their environment. Her contributions are particularly relevant for students and engineers interested in embedded AI, sensor-based navigation, and the intersection of algorithm efficiency with real-world robotic constraints.
Research Focus
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Top Papers
- 1