Papers
2
Total Citations
12
H-Index
1
About
Dr. Yefeng Yang is a rising robotics researcher whose work centers on intelligent path planning and autonomous obstacle avoidance for robotic systems. His primary contributions lie in developing sampling-efficient algorithms and actor-critic reinforcement learning frameworks that enable robots to navigate complex environments with improved motion feasibility. Notably, his 2024 paper on "Sampling-efficient path planning and improved actor-critic-based obstacle avoidance for autonomous robots" has already garnered 11 citations, reflecting its early impact in the field. In his most recent 2025 work, Yang addresses a critical gap in traditional path-planning algorithms—which often focus solely on end-effector motion while neglecting complete manipulator collision avoidance. By integrating goal-driven and potential field strategies, his approach reduces time complexity and overcomes the local optima traps that plague conventional methods. This work promises to enhance the safety and efficiency of robotic manipulators in real-world applications. Yang’s research is particularly valuable for students and engineers seeking practical, computationally efficient solutions for autonomous robotics, bridging the gap between theoretical planning and real-time execution.
Research Focus
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Top Papers
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