This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Motion planning and its optimization is vital and difficult for redundant robot manipulator in
an environment with obstacles. In this article, a general motion planning framework that …

In this letter, we present a novel navigation system of unmanned ground vehicle (UGV) for
local path planning based on deep reinforcement learning. The navigation system …

In this paper, we propose a novel deep reinforcement learning (DRL) method for optimal
path planning for mobile robots using dynamic programming (DP)-based data collection …

Coordinated defensive escorts can aid a navigating payload by positioning themselves
strategically in order to maintain the safety of the payload from obstacles. In this letter, we …

Deep Reinforcement Learning (DRL) is critical in modern Artificial Intelligence (AI), powering
innovations from gaming to autonomous vehicles. As DRL continues its rapid ascent …

Deep Reinforcement Learning (DRL) has proven to be a very strong technique with results
in various applications in recent years. Especially the achievements in the studies in the field …

Using mobile robots for transportation in a warehouse is becoming more and more common.
Compared with human staff, these robots can handle the goods more accurately and more …

DAMON leverages manifold learning and variational autoencoding to achieve obstacle
avoidance, allowing for motion planning through adaptive graph traversal in a pre-learned …

Coordinated defensive escorts can aid a navigating payload by positioning themselves in
order to maintain the safety of the payload from obstacles. In this paper, we present a novel …