Kaixin Chai

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About

Kaixin Chai is a robotics researcher specializing in motion planning for autonomous ground vehicles operating in complex, unstructured environments. Their work focuses on solving the dual challenges of traversability assessment and dynamic modeling for robots on uneven terrain. Chai’s most influential contribution, "An Efficient Trajectory Planner for Car-Like Robots on Uneven Terrain" (2023), has garnered 27 citations, establishing a foundation for safe and efficient navigation where traditional planners fail. Building on this, Chai extended their approach to tractor-trailer robots in "Tracailer: An Efficient Trajectory Planner for Tractor-Trailer Robots in Unstructured Environments" (2025), addressing the intricate kinematics of multi-body systems with hitches—a critical step for enhancing transportation capabilities in off-road logistics. By integrating terrain-aware dynamics into real-time trajectory optimization, Chai’s work bridges the gap between theoretical motion planning and practical deployment on rugged landscapes. Their research is pivotal for applications in agriculture, search-and-rescue, and planetary exploration, where reliable autonomy depends on understanding how ground shape and robot dynamics interact.