Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

The Cloud infrastructure and its extensive set of Internet-accessible resources has potential
to provide significant benefits to robots and automation systems. We consider robots and …

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

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

A defining feature of sampling-based motion planning is the reliance on an implicit
representation of the state space, which is enabled by a set of probing samples …

This article describes motion planning networks (MPNet), a computationally efficient,
learning-based neural planner for solving motion planning problems. MPNet uses neural …

Robotic applications nowadays are widely adopted to enhance operational automation and
performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0 …

Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics
applications such as self-driving cars. Existing motion planning methods become ineffective …

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 …

Cloud robotics has recently emerged as a collaborative technology between cloud
computing and service robotics enabled through progress in wireless networking, large …