Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that
leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and …

Autonomous wheeled-legged robots have the potential to transform logistics systems,
improving operational efficiency and adaptability in urban environments. Navigating urban …

Natural environments such as forests and grasslands are challenging for robotic navigation
because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this …

Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell
failures, facilitate battery deployment, and promote low-carbon economies. Despite the …

Despite the rapid advancement of navigation algorithms, mobile robots often produce
anomalous behaviors that can lead to navigation failures. The ability to detect such …

Navigation is one of the most heavily studied problems in robotics and is conventionally
approached as a geometric mapping and planning problem. However, real-world navigation …

We present TerraPN, a novel method that learns the surface properties (traction, bumpiness,
deformability, etc.) of complex outdoor terrains directly from robot-terrain interactions through …

Neural networks (NNs) are widely used for object classification in autonomous driving.
However, NNs can fail on input data not well represented by the training dataset, known as …

Traversability illustrates the difficulty of driving through a specific region and encompasses
the suitability of the terrain for traverse based on its physical properties, such as slope and …