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About

Jianyi Kong is a prolific robotics and intelligent systems researcher whose work spans robot trajectory optimization, sensory feedback, rehabilitation robotics, and human-machine interaction. His most influential contribution, "Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots" (2022, 156 citations), established him as a leading voice in applying evolutionary computation to mobile robot path planning within manufacturing environments. Complementing this, his work on particle swarm optimization for inverse kinematics solutions (78 citations) further demonstrates his expertise in intelligent algorithms for robot control. Kong has made notable strides in sensory technology, developing a fiber Bragg grating-based fingertip force sensor (90 citations) and advancing sEMG-driven hand interfaces integrated with IoT and haptic feedback (80 citations). His research extends into human-robot interaction through gesture recognition and deep learning-based SLAM for simultaneous localization and mapping. An early advocate for rehabilitation robotics, Kong authored multiple influential reviews on upper and lower limb rehabilitation systems, helping to map the field for future researchers. With over 500 cumulative citations across diverse yet interconnected domains, Kong's career reflects a sustained commitment to bridging intelligent algorithms, sensor innovation, and real-world robotic applications.