Sajad Saeedi

Environmental sampling robots face a 27.7% gap between planned and executed cost savings in time-varying currents, revealing a critical need for integrated simulation of hydrodynamics, particle transport, and vehicle dynamics.

MVOFormer outperforms traditional learning-based MVO methods by achieving superior zero-shot generalization and robustness through a novel flow-semantic architecture.