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About

Xinsheng Ge is a robotics and control systems researcher whose work spans nonholonomic motion planning, space robotics, and autonomous robot locomotion. Over more than a decade of scholarship, Ge has made consistent contributions to the application of evolutionary computation and optimization techniques—particularly genetic algorithms and particle swarm optimization—to complex robotic control problems that resist traditional analytical approaches. His early and most influential contributions addressed nonholonomic motion planning, reformulating trajectory generation as optimal control problems solvable through wavelet approximation combined with genetic algorithms, work that attracted citations across the space and mobile robotics communities. He extended these methods to wheeled mobile robots and biped gait planning using spline-based PSO frameworks, demonstrating the versatility of his computational approach across diverse robotic platforms. Ge's later research tackled physically demanding systems, including pneumatic hexapod robots designed for disaster-zone rescue in flammable environments, and free-floating space robots operating under initial state uncertainty—both works published in 2017–2018 and already accruing meaningful citations. His use of Lie group methods for hexapod kinematics further reflects a rigorous mathematical foundation underlying his engineering contributions. Collectively, his body of work offers students a valuable model for applying metaheuristic optimization to real-world robotic systems where conventional methods fall short.