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About

Xiangmo Zhao is a leading researcher in robotics, autonomous navigation, and intelligent systems, with a focus on advancing path planning, locomotion control, and environmental perception. His major contributions include developing an improved RRT-Connect path planning algorithm for robotic arms in Industry 4.0 applications, which has garnered 46 citations and addresses critical challenges in automated sampling for emission detection. Zhao has also pioneered bio-inspired locomotion strategies for hexapod robots, proposing a trajectory correction methodology using Least Squares Support Vector Machines (LS-SVM) to mitigate body trajectory errors from semi-round rigid feet, and an ant-inspired sensory strategy for turning and deviation correction—work that has earned 16 and 10 citations, respectively. In autonomous driving, he has introduced innovative place recognition methods, such as LGD, which fuses local and global descriptors for fast and accurate loop closure detection (9 citations), and a modular, loosely coupled approach to loop closure that enhances reliability in autonomous vehicles (4 citations). His earlier work on visual odometry using trifocal tensors for on-road vehicles (2 citations) further underscores his expertise in precise positioning. Zhao’s research is notable for its interdisciplinary blend of robotics, bio-mimicry, and AI, with a clear impact on real-world automation and autonomous systems.