Motion planning is a fundamental problem in autonomous robotics that requires finding a
path to a specified goal that avoids obstacles and takes into account a robot's limitations and …

The world is becoming more digitised with the rise of modular robotic systems. Therefore,
with the increasing demands and needs for robotics, the modular robotic domain was …

Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and
research applications. To fully leverage their potential, a key challenge is planning missions …

Informative path planning (IPP) is an NP-hard problem, which aims at planning a path
allowing an agent to build an accurate belief about a quantity of interest throughout a given …

This work reports ondevelo** a deep inverse reinforcement learning method for legged
robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive …

Most of the existing robotic exploration schemes use occupancy grid representations and
geometric targets known as frontiers. The occupancy grid representation relies on the …

This article develops a Bayesian continuous 3D semantic occupancy map from noisy point
clouds by generalizing the Bayesian kernel inference model for building occupancy maps, a …

Robot exploration in GPS-denied environments is a significant challenge due to the lack of
reliable localization strategies. In this article, we propose a dual-layer planning approach …

Frontier-based exploration is widely adopted for exploring an unknown region. The
conventional frontier-based exploration for a mobile robot may collide with three …