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About

Yangxin Teng is a rising researcher in autonomous robotics, specializing in intelligent path planning for mobile and multi-robot systems. Their work addresses critical challenges in navigating complex, dynamic environments while accounting for varying task hazard levels and road conditions. Teng’s most impactful contribution is the development of a Hybrid Adaptive Genetic Algorithm (HAGA), which integrates task-risk modeling with evolutionary optimization to generate optimal global paths—a paper that has already garnered 22 citations since 2024. They further advanced the field with a dual-layer symmetric path planning system combining an improved neural network with the DWA algorithm for multi-robot coordination (14 citations, 2025), and a novel method fusing dual-layer fuzzy control with genetic algorithms for safety-aware navigation (6 citations, 2025). Teng’s work stands out for its practical integration of artificial intelligence, fuzzy logic, and bio-inspired neural networks, offering scalable solutions for real-world robotics applications. Their research is quickly gaining recognition for bridging theoretical optimization with operational safety, making Teng a notable emerging voice in autonomous navigation.