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About

Jianhao Tan is a leading researcher in autonomous robotics, specializing in path planning for mobile and flying robots in complex, dynamic environments. His work bridges the gap between classical algorithms and real-world adaptability, with a focus on neural dynamics and hybrid optimization methods. Tan’s most influential paper, “Autonomous mobile robot path planning in unknown dynamic environments using neural dynamics” (2020), has garnered 55 citations, demonstrating its significant impact on the field. In this work, he pioneered a neural dynamics-based approach that enables robots to navigate unpredictable surroundings without pre-mapped data, a critical advancement for autonomous systems. His earlier contributions include a fusion algorithm combining A* with artificial potential fields for rotary-wing flying robots in 3D mountain environments (2016, 17 citations), and an ant colony algorithm integrated with potential fields for three-dimensional path planning (2015, 4 citations). These studies showcase Tan’s ability to enhance traditional methods—like A* and ant colony optimization—by embedding them with potential field techniques, resulting in more efficient, collision-free navigation. His research is foundational for students and engineers working on drones, autonomous vehicles, and rescue robotics, offering practical solutions for real-world spatial challenges.