Papers
1
Total Citations
18
H-Index
1
About
Shi Han is a researcher in robotics and autonomous navigation, with a primary focus on real-time path planning for mobile robots operating in dynamic environments. Their most notable contribution is the development of a hybrid path planning method that combines the artificial potential field method with a biased target Rapidly-exploring Random Tree (RRT) algorithm. This work, published in 2021 and cited 18 times, addresses critical challenges in robotics—specifically, improving the efficiency and effectiveness of collision-free navigation when obstacles move unpredictably. By integrating the goal-oriented bias of the RRT algorithm with the reactive nature of potential fields, Han’s approach enables robots to generate smoother, more adaptive paths in real time. This research is particularly impactful for applications in autonomous vehicles, warehouse logistics, and service robotics, where safe and rapid decision-making is essential. Shi Han’s work stands out for its practical, algorithm-level innovation that bridges theoretical planning with real-world dynamic constraints, offering a valuable tool for engineers and researchers seeking robust navigation solutions. Their contributions continue to influence the development of more intelligent and responsive autonomous systems.
Research Focus
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Top Papers
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