Chaozheng Wang

University of Aberdeen, China Academy of Launch Vehicle Technology, Beijing Institute of Technology

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About

Chaozheng Wang’s research lies at the intersection of robotics, manipulation, and autonomous navigation, with a focus on enabling machines to adapt intelligently to dynamic, unstructured environments. His most cited work, *Adapting Everyday Manipulation Skills to Varied Scenarios* (2019, 8 citations), tackles a core challenge in robotics: executing tool-using skills when objects vary and no prior knowledge is available. By relying solely on point cloud data, Wang’s system empowers robots to interpret and manipulate unfamiliar tools and targets—a critical step toward general-purpose service robots. In parallel, his contributions to mobile robot path planning, particularly through the integration of fuzzy logic with artificial potential fields (APF), address the notorious local minima problem that plagues traditional APF methods. His 2019 paper on this topic (6 citations) introduces a heading fuzzy controller to ensure smoother, more reliable navigation in complex environments. Earlier, Wang applied a modified APF to snake robots (2017, 4 citations), leveraging their multi-joint redundancy for effective 2D obstacle avoidance. Together, these works demonstrate Wang’s commitment to practical, real-time solutions that push robots from controlled labs into the unpredictable real world.