Active simultaneous localization and mapping (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

Simultaneous localization and mapping responds to the problem of building a map of the
environment without any prior information and based on the data obtained from one or more …

Autonomous exploration of subterranean environments remains a major challenge for
robotic systems. In response, this paper contributes a novel graph‐based subterranean …

Autonomous exploration is a fundamental problem for various applications of unmanned
aerial vehicles (UAVs). Existing methods, however, were demonstrated to insufficient …

The ability to plan informative paths online is essential to robot autonomy. In particular,
sampling-based approaches are often used as they are capable of using arbitrary …

Exploration is an important aspect of robotics, whether it is for mapping, rescue missions, or
path planning in an unknown environment. Frontier Exploration planning (FEP) and …

This paper presents a novel strategy for autonomous teamed exploration of subterranean
environments using legged and aerial robots. Tailored to the fact that subterranean settings …

This paper presents a novel strategy for autonomous graph-based exploration path planning
in subterranean environments. Attuned to the fact that subterranean settings, such as …

This paper presents a novel path planning strategy for fast and agile exploration using aerial
robots. Tailored to the combined need for large-scale exploration of challenging and …

Autonomous exploration requires robots to generate informative trajectories iteratively.
Although sampling-based methods are highly efficient in unmanned aerial vehicle …