Nabih Pico

• Doctor of Philosophy
• Research Professor at Sungkyunkwan University

This paper proposes a design methodology for a six-wheeled rover that adapt to different stairs and maintain its stability based on the robot’s parameters, the kinematics constraints, the maximum height, and the minimum length of the step required to climb up and down. We also propose an emergency controller to prevent falls during the climb up or...

This paper presents an alarm system and teleoperation control framework, comparing ROS 1 and ROS 2 within a local network to mitigate the risk of robots failing to reach their goals during autonomous navigation. Such failures can occur when the robot moves through irregular terrain, becomes stuck on small steps, or approaches walls and obstacles wi...

This study presents an approach to autonomous navigation for wheeled robots, combining radar-based dynamic obstacle detection with a BiGRU-based deep reinforcement learning (DRL) framework. Using filtering and tracking algorithms, the proposed system leverages radar sensors to cluster object points and track dynamic obstacles, enhancing precision b...

This paper presents a software architecture to implement a task-motion planning system that can improve human-robot interactions by including social behavior when social robots provide services related to object manipulation to users. The proposed system incorporates four main modules: knowledge reasoning, perception, task planning, and motion plan...

The primary contributions of this paper include the systematic organization of existing research and an in-depth analysis of significant milestones in the field of CPPS, leading to the development of a taxonomy for categorizing various drivers. Furthermore, this paper elucidates current strategies for defining clear terms, models, methodologies, an...

The primary contributions of this paper include the systematic organization of existing research and an in-depth analysis of significant milestones in the field of CPPS, leading to the development of a taxonomy for categorizing various drivers. Furthermore, this paper elucidates current strategies for defining clear terms, models, methodologies, an...

Robot navigation in crowded environments has recently benefited from advances in deep reinforcement learning (DRL) approaches. However, it still presents a challenge to designing socially compliant robot behavior. Avoiding collisions and the difficulty of predicting human behavior are crucial and challenging tasks while the robot navigates in a con...

This paper explores the development of intelligent and adaptive interaction control strategies for physical human-robot-environment interaction. Collaborative robots with variable admittance control have shown promising results in providing compliant behavior of the robot's end-effector in response to human guidance. However, instability can arise...

Accurate dynamic model is critical for collaborative robots to achieve satisfactory performance in model-based control or other applications such as dynamic simulation and external torque estimation. Such dynamic models are frequently restricted to identifying important system parameters and compensating for nonlinear terms. Friction, as a primary...

This work proposes an online task-scheduling method using mixed-integer programming for a multi-tasking problem regarding a dual-arm cooking robot in a controlled environment. Given each task’s processing time, their location in the working space, dependency, the required number of arms, and the kinematic constraints of the dual-arm robot, the prop...

The objective of this work is to control a delivery robot equipped with a passive bogie that can successfully climb up steps of various sizes and move on uneven terrain in outdoor environments. The kinematic model of a six-wheel mobile robot is described in detail. Jacobian matrices and inverse kinematics are obtained to get the velocity of each wh...