Papers
2
Total Citations
178
H-Index
2
About
Christopher Tay is a leading researcher in autonomous navigation, specializing in probabilistic motion planning and risk-aware decision-making for dynamic, uncertain environments. His foundational work, "Probabilistic navigation in dynamic environment using Rapidly-exploring Random Trees and Gaussian processes" (2008, 129 citations), introduced a pioneering algorithm that integrates Gaussian processes to model learned motion patterns of moving obstacles, enabling safer path planning through unpredictable spaces. This approach marked a significant shift from deterministic to probabilistic representations, enhancing robustness in real-world applications. Tay further advanced the field with "Risk based motion planning and navigation in uncertain dynamic environment" (2010, 49 citations), where he formalized risk-aware strategies that balance efficiency and safety, moving beyond reactive avoidance to proactive, informed navigation. His contributions have been instrumental in bridging probabilistic mapping and prediction methods, improving the quality of autonomous systems in complex settings like robotics and autonomous vehicles. Tay’s work remains highly influential, cited for its innovative fusion of machine learning and motion planning, and continues to inspire researchers tackling the challenges of safe, intelligent navigation in uncertain worlds.
Research Focus
Key Achievements
Top Papers
- 1
- 2Risk based motion planning and navigation in uncertain dynamic environment49 citations · 2010