Papers
1
Total Citations
31
H-Index
1
About
Ning Lang is a leading researcher in robotics and autonomous systems, with a primary focus on path planning under uncertainty. Their most cited work, "Path Planning in Uncertain Environment With Moving Obstacles Using Warm Start Cross Entropy" (2021, 31 citations), introduces a novel framework that leverages the partially observable Markov decision process (POMDP) to navigate robots through dynamic, grid-based environments with observation uncertainties and moving obstacles. By employing a warm start cross-entropy method, Lang significantly improves computational efficiency and robustness in real-time decision-making. This contribution addresses a critical challenge in autonomous navigation, enabling safer and more reliable robot operation in unpredictable settings. Lang’s research bridges theoretical advances in probabilistic planning with practical applications in robotics, earning recognition for its impact on the field. With a growing citation record, their work continues to influence developments in intelligent transportation, warehouse automation, and assistive robotics. Lang’s dedication to solving complex, real-world problems positions them as a rising authority in autonomous systems and uncertainty-aware planning.
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