Ji Xiao-Ming

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About

Ji Xiao-Ming is a robotics researcher whose work centers on intelligent motion planning and optimization algorithms for autonomous systems. His most cited contribution, "A Robot Obstacle Avoidance Method Based on Improved Genetic Algorithm" (2018), addresses a fundamental challenge in robotics: enabling robots to navigate complex environments with enhanced spatial awareness. By refining genetic algorithm (GA) techniques, Ji introduced a novel evolutionary adaptation model that significantly improves a robot’s ability to avoid obstacles while optimizing kinematic planning and design. This work, garnering 15 citations, has provided a practical framework for integrating bio-inspired computation into real-time robotic control systems. Ji’s research bridges the gap between theoretical optimization and applied robotics, offering scalable solutions for autonomous navigation in dynamic settings. His focus on genetic evolutionary adaptation highlights a commitment to developing robust, adaptive algorithms that enhance machine autonomy. For students and researchers exploring the intersection of artificial intelligence and robotics, Ji Xiao-Ming’s work serves as a valuable reference point for leveraging evolutionary strategies to solve spatial planning problems.