Recent technological advances in hardware design of the robotic platforms enabled the
implementation of various control modalities for improved interactions with humans and …

In this article, we present a metaheuristic-based control framework, called beetle antennae
olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance …

In recent years, numerous studies have been conducted to analyze how humans
subconsciously optimize various performance criteria while performing a particular task …

In this paper, we propose a topology of Recurrent Neural Network (RNN) based on a
metaheuristic optimization algorithm for the tracking control of mobile-manipulator while …

This article proposes a control algorithm for obstacle avoidance and trajectory tracking for a
redundant‐manipulator in smart‐homes. The redundancy provides dexterity and flexibility …

Human-like behavior has emerged in the robotics area for improving the quality of Human-
Robot Interaction (HRI). For the human-like behavior imitation, the kinematic map** …

Computing and modifying in real-time the trajectory of an industrial robot involved in a
Human-Robot Collaboration (HRC) scenario is a challenging problem, mainly because of …

It is a common belief that service robots shall move in a human-like manner to enable
natural and convenient interaction with a human user or collaborator. In particular, this …

This paper proposes a real-time human inverse kinematics approach based on the
Gaussian Process (GP) to address the open problem of finding appropriate inverse …

In this paper, we propose a recurrent neural network (RNN) for the tracking control of
surgical robots while satisfying remote center-of-motion (RCM) constraints. RCM constraints …