Qisen Chai

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About

Qisen Chai is a rising researcher in mobile robotics, specializing in sampling-based path planning algorithms for complex environments. His work focuses on overcoming the fundamental challenge of navigating narrow passages—a notoriously difficult problem for traditional planners like RRT and PRM. Chai’s major contributions include the development of RJ-RRT, an improved rapidly exploring random tree algorithm that significantly enhances path-finding efficiency in constrained spaces (28 citations). He has also pioneered the integration of particle swarm optimization with probabilistic roadmaps, creating a hybrid approach that intelligently guides sampling in narrow corridors (16 citations). Most recently, Chai advanced practical robotics by incorporating clothoid curves into RRT*, enabling the generation of smooth, kinematically feasible paths for mobile robots under real-world motion constraints (14 citations). His work directly addresses the gap between theoretical planning and real-world deployment, making autonomous navigation more reliable and efficient. With a growing citation record and a clear trajectory of innovation, Chai is establishing himself as a key contributor to the next generation of intelligent motion planning systems.