Benying Tan

Guilin University of Electronic Technology

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Total Citations

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About

Benying Tan is a researcher focused on advancing autonomous navigation and robotics, with a particular emphasis on path planning algorithms. Their major contribution lies in addressing critical limitations of the artificial potential field (APF) method—a widely used technique for robot local path planning. In their notable 2023 work, Tan introduced an enhanced approach called the extended random artificial potential field, which effectively mitigates two persistent challenges: the tendency to fall into local minima and the generation of paths that violate robot kinematic constraints. This work has garnered early attention with 3 citations, signaling its relevance to the robotics community. By improving the reliability and feasibility of real-time path planning, Tan’s research holds practical implications for autonomous systems, from mobile robots to unmanned vehicles. Their innovative solution to a classic problem demonstrates a commitment to bridging theoretical algorithms with real-world applicability, making their work a valuable reference for students and researchers seeking to overcome fundamental hurdles in robotic navigation.