The capabilities of autonomous mobile robotic systems have been steadily improving due to
recent advancements in computer science, engineering, and related disciplines such as …

Mapping the environment is a powerful technique for enabling autonomy through
localization and planning in robotics. This article seeks to provide a global overview of …

We present a novel method for reliable robot navigation in uneven outdoor terrains. Our
approach employs a fully-trained Deep Reinforcement Learning (DRL) network that uses …

Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead
of manually designing costs based on terrain features, existing methods learn terrain …

Due to the high price and heavy energy consumption of GPUs, deploying deep models on
IoT devices such as microcontrollers makes significant contributions for ecological AI …

Estimating the traversability of terrain should be reliable and accurate in diverse conditions
for autonomous driving in off-road environments. However, learning-based approaches …

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 …

We introduce the UT Campus Object Dataset (CODa), a mobile robot egocentric perception
dataset collected on the University of Texas Austin Campus. Our dataset contains 8.5 hours …

A key challenge in off-road navigation is that even visually similar terrains or ones from the
same semantic class may have substantially different traction properties. Existing work …

This letter presents an integrated navigation and control strategy for an autonomous surface
vehicle (ASV) to operate in narrow waterways without relying on GPS. The proposed method …