A framework for robotized teleoperated tasks

"Premio al mejor artículo presentado en ROBOT 2011" atorgat pel Grupo de Robótica, Visión y Control de la Universidad de Sevilla, la Universidad Pablo Olavide i el Centro Avanzado de Tecnologías Aeroespaciales.Teleoperation systems allow the extension of the human operator’s sensing and manipulative capability into a remote environment to perform tasks at a distance, but the time-delays in the communications affect the stability and transparency of such systems. This work presents a teleoperation framework in which some novel tools, such as nonlinear controllers, relational positioning techniques, haptic guiding and augmented reality, are used to increase the sensation of immersion of the human operator in the remote site. Experimental evidence supports the advantages of the proposed framework.Award-winningPostprint (published version

Recent Advancements in Augmented Reality for Robotic Applications: A Survey

Robots are expanding from industrial applications to daily life, in areas such as medical robotics, rehabilitative robotics, social robotics, and mobile/aerial robotics systems. In recent years, augmented reality (AR) has been integrated into many robotic applications, including medical, industrial, human–robot interactions, and collaboration scenarios. In this work, AR for both medical and industrial robot applications is reviewed and summarized. For medical robot applications, we investigated the integration of AR in (1) preoperative and surgical task planning; (2) image-guided robotic surgery; (3) surgical training and simulation; and (4) telesurgery. AR for industrial scenarios is reviewed in (1) human–robot interactions and collaborations; (2) path planning and task allocation; (3) training and simulation; and (4) teleoperation control/assistance. In addition, the limitations and challenges are discussed. Overall, this article serves as a valuable resource for working in the field of AR and robotic research, offering insights into the recent state of the art and prospects for improvement

Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection

In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation

On-line collision avoidance for collaborative robot manipulators by adjusting off-line generated paths: An industrial use case

Human–robot collision avoidance is a key in collaborative robotics and in the framework of Industry 4.0. It plays an important role for achieving safety criteria while having humans and machines working side-by-side in unstructured and time-varying environment. This study introduces the subject of manipulator’s on-line collision avoidance into a real industrial application implementing typical sensors and a commonly used collaborative industrial manipulator, KUKA iiwa. In the proposed methodology, the human co-worker and the robot are represented by geometric primitives (capsules). The minimum distance and relative velocity between them is calculated, when human/obstacles are nearby the concept of hypothetical repulsion and attraction vectors is used. By coupling this concept with a mathematical representation of robot’s kinematics, a task level control with collision avoidance capability is achieved. Consequently, the off-line generated nominal path of the industrial task is modified on-the-fly so the robot is able to avoid collision with the co-worker safely while being able to fulfill the industrial operation. To guarantee motion continuity when switching between different tasks, the notion of repulsion-vector-reshaping is introduced. Tests on an assembly robotic cell in automotive industry show that the robot moves smoothly and avoids collisions successfully by adjusting the off-line generated nominal paths

Scene perception and motion planning through robotic vision

The use of robotic vision is extensive in any field. This thesis aims to apply the theoretical concepts of the coverage strength model in order to achieve a task using robotic vision. The task mentioned is motion planning for the robotic arm such that it does not collide with an obstacle while performing its operation. This research can be applied to robots in an industrial work cell or an unmanned system. Hand in eye configuration is used to control the robotic motion. Different levels of calibration are implemented, as a camera network is involved. An algorithm is used to implement the motion planning. This algorithm also involves the pose estimation of the objects used in the work cell. The work cell is modeled with a camera network and a robot. The calibration helps to visualize the position of the various entities of the work cell. The algorithm is applied to the physical experimental setup and results are recorded

Semi-Autonomous Behaviour Tree-Based Framework for Sorting Electric Vehicle Batteries Components

The process of recycling electric vehicle (EV) batteries currently represents a significant challenge to the waste management automation industry. One example of it is the necessity of removing and sorting dismantled components from EV battery pack. This paper proposes a novel framework to semi-automate the process of removing and sorting different objects from an EV battery pack using a mobile manipulator. The work exploits the Behaviour Trees model for cognitive task execution and monitoring, which links different robot capabilities such as navigation, object tracking and motion planning in a modular fashion. The framework was tested in simulation, in both static and dynamic environments, and it was evaluated based on task time and the number of objects that the robot successfully placed in the respective containers. Results suggested that the robot’s success rate in accomplishing the task of sorting the battery components was 95% and 82% in static and dynamic environments, respectively