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About

Shupeng Lai is a robotics researcher whose work spans autonomous navigation, aerial robotics, and robust flight control — areas where his contributions have meaningfully advanced the field's practical capabilities. His foundational work on model predictive local motion planning introduced boundary state constrained primitives, enabling mobile robots to rapidly replan trajectories under computational constraints, accumulating 42 citations since 2019. Complementing this, his GPU-accelerated incremental Euclidean Distance Transform framework (28 citations) addresses real-time volumetric mapping challenges critical for safe robot navigation in dynamic environments. Lai has also made significant strides in multi-UAV systems, co-developing the robust formation control framework behind Singapore's first outdoor night drone show — a high-profile achievement demonstrating the real-world viability of coordinated autonomous aerial vehicles, earning 35 citations. More recently, his neural moving horizon estimation approach (29 citations) tackles disturbance rejection in quadrotor flight without requiring ground-truth disturbance data during training, pushing the boundaries of adaptive flight control. His broader research portfolio further explores formal task specification through linear temporal logic and trajectory generation for integrator chains. Collectively, Lai's work reflects a consistent commitment to bridging theoretical rigor with deployable autonomous systems.