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About

Baojia Chen is a leading researcher in robotics and intelligent automation, with a focus on mobile robot trajectory optimization, humanoid manipulation, and visual SLAM (Simultaneous Localization and Mapping). Chen’s most influential work, a 2022 study on genetic algorithm-based trajectory optimization for digital twin robots, has garnered 156 citations, establishing a foundation for precision material handling in manufacturing. Another key contribution is the development of a grasping posture method for humanoid manipulators based on target shape analysis and force closure (75 citations), which addresses the challenge of unstructured environments. Chen has also advanced target localization through RGBD SLAM and object detection (35 citations) and multi-objective mapping using deep learning (32 citations), enhancing map readability and interactivity for intelligent robots. More recently, Chen introduced a fuzzy dynamic window algorithm for local path planning (27 citations) and a neural network-based Markov decision process for dual manipulator grasping detection (18 citations). Notable achievements include multi-scale feature fusion for substation inspection robots (12 citations) and novel probabilistic collision detection using HNSW. With a total of over 360 citations across nine papers, Chen’s work is pivotal for advancing autonomous robotics in industrial and smart grid applications.