5 accepted papers
Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnosti
Relay robots are crucial for extending communication when a client robot performs long-range missions. However, existing network quality prediction models and relay planning methods often struggle with real-time operation due to their high computational cost and poor adaptability to frequently chang
Relay robots are crucial for extending communication when a client robot performs long-range missions. However, existing network quality prediction models and relay planning methods often struggle with real-time operation due to their high computational cost and poor adaptability to frequently chang…
Deep learning methods based on backward stochastic differential equations (BSDEs) have emerged as competitive alternatives to physics-informed neural networks (PINNs) for solving high-dimensional partial differential equations (PDEs). By leveraging probabilistic representations, BSDE approaches can …
This letter proposes a dual-mode planner for localizing gas sources using a mobile sensor in unknown indoor spaces. The complexity of indoor environments creates constraints on search paths, leading to situations where no valid paths can be generated, which are termed as dead end in this letter. The