Lin Chang

Papers

Total Citations

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About

Lin Chang’s research centers on robotics, path planning, and optimization algorithms, with a particular focus on overcoming the limitations of traditional artificial potential field methods. In his most-cited work, “Research on multi-objective path planning of a robot based on artificial potential field method” (2018, 11 citations), Chang addresses a critical challenge in autonomous navigation: the tendency of conventional approaches to stall in zero potential fields when encountering complex obstacles, failing to guarantee optimal routes. He proposes a novel multi-objective framework that integrates multiple optimization criteria, enabling more robust and efficient path generation in cluttered environments. This contribution is especially valuable for applications in automated manufacturing, service robotics, and autonomous vehicles, where reliable obstacle avoidance is paramount. While his citation count reflects an emerging career, Chang’s work demonstrates a clear ability to identify and solve practical engineering problems, laying the groundwork for more adaptive and intelligent robotic systems. His research continues to influence scholars working on heuristic and swarm-based navigation methods, marking him as a promising voice in the field of intelligent robotics.