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About

Ruitong Sun is a robotics and autonomous systems researcher whose work centers on computational path planning algorithms for both robotic manipulators and mobile robots. With a focus on overcoming fundamental limitations in classical optimization and sampling-based methods, Sun has made meaningful contributions to the field by developing hybrid and enhanced algorithmic frameworks that improve convergence speed, search efficiency, and obstacle avoidance capability in complex environments. Sun's most influential work, cited 66 times, introduced an improved potential function-based RRT* algorithm that addresses the slow convergence and poor search efficiency inherent in conventional P_RRT* approaches for manipulator path planning. Complementing this, Sun has advanced particle swarm optimization by integrating differential evolution strategies to combat premature convergence in mobile robot navigation, work that has collectively attracted over 55 citations across multiple publications. A notable earlier contribution proposed a hybrid artificial potential field and RRT algorithm, earning 19 citations for its innovative approach to three-dimensional robotic arm motion planning. Across these publications, Sun demonstrates a consistent methodology: identifying algorithmic bottlenecks in established techniques and engineering targeted improvements. With over 140 cumulative citations, Sun's research is increasingly recognized as valuable reading for engineers and students working at the intersection of robotics, intelligent control, and computational motion planning.