In recent years, autonomous mobile robots have significantly increased in prevalence due to
their ability to augment and diversify the workforce. One critical aspect of their operation is …

Over the years, deep reinforcement learning (DRL) has shown great potential in mapless
autonomous robot navigation and path planning. These DRL methods rely on robots …

The optimality of Bayesian filtering relies on the completeness of prior models, while deep
learning holds a distinct advantage in learning models from offline data. Nevertheless, the …

The Kalman filter's performance deteriorates in the existence of slowly time-varying and
unknown measurement and process noise covariances. A simplified strong tracking square …

This study investigates the 2-D relative localization problem, which estimates the relative
orientation and position between two moving robots using inter-robot range measurements …

Feature extraction or localization based on the fiducial marker could fail due to motion blur in
real-world robotic applications. To solve this problem, a lightweight generative adversarial …

The event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL)
problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated …

This paper proposes approximate inference particle filtering for mobile robot simultaneous
localization and mapping (SLAM) with landmarks. Range-bearing measurements are …

The optimality of Bayesian filtering relies on the completeness of prior models, while deep
learning holds a distinct advantage in learning models from offline data. Nevertheless, the …

To address the challenge of simultaneous localization and mapping (SLAM) in the presence
of heavy-tailed noise, this paper introduces a robust probability hypothesis density (PHD) …