Cable elasticity can significantly impact the accuracy of Cable-Driven Parallel Robots (CDPRs). However, it’s frequently disregarded as negligible in CDPR simulations and designs. In this paper, we propose a numerical approach, referred to as SEECR, which is designed to estimate the behavior of a CDPR featuring elastic cables while ensuring the Static Equilibrium (SE) of the Moving-Platform (MP). By modeling the cables as elastic springs, the proposed approach correctly predicts which cables become slack, estimates the tension distribution among cables and computes unwanted MP motions, allowing to predict the impact of design choices. The results have been validated experimentally on two cable types and configurations.

Cable-Driven Parallel Robots (CDPRs) are well-adapted to large workspaces since they replace rigid links by cables. However, they lack in positioning accuracy and new control methods are necessary to achieve profile-following tasks. This paper presents a control scheme designed for these tasks, relying on a combination of accurate boarded distance sensors and of a less accurate remote camera. The profile-following task is divided into two subtasks that are partially conflicting: maintaining a parallel orientation and a constant distance with the surface to follow, and following a trajectory between two points on the surface. The data fusion to solve the redundancy is based on the Gradient Projection Method. This control scheme is validated experimentally on a CDPR prototype and shown to provide the expected behaviour.

Cable-driven parallel robots (CDPRs) are a type of parallel robots, where cables are used instead of rigid links. This leads to many advantages, such as large workspace, low mass in motion and simple reconfiguration. The drawbacks are accuracy issues and complex cable management. Indeed, it is usual that cables become slack. That can be caused by, for example, cable mass, uncertainties in the system, and a higher number of cables than the number of degrees of freedom of the moving-platform. This reduces CDPR stiffness and degree of actuation. While visual servoing provides good accuracy and is robust to different perturbations in the system and to modeling errors, it does not deal with cable slackness. Thus, a CDPR with visual servoing can become underactuated due to cable slack. We propose in this paper to enrich visual servoing with a tension correction algorithm. Experimental results show reduction of slackness and thus avoiding slackness-related trajectory perturbations and loss of stability.

Cable-Driven Parallel Robots (CDPRs) are parallel robots with rigid links replaced by cables. As for most parallel robots the determination of the analytical solutions to the direct geometrico-static model (DGSM) is a difficult task that is often not feasible online. However, the knowledge of the moving-platform (MP) pose is necessary in order to control the CDPR, e.g. with visual servoing. When the MP pose measurement is not available, an estimation can be sufficient. This paper compares three estimation methods: (a) control-based; (b) image-based; and (c) model-based. The three methods are implemented experimentally with an open-loop velocity controller and a closed-loop visual servoing controller. Overall, very good results are shown with model-based and control-based methods for both controllers. Finally, it is shown that the visual servoing controller leads to a better accuracy of the robot than the velocity controller.

Shared control is a key technology for various robotic applications in which a robotic system and a human operator are meant to collaborate efficiently. In order to achieve efficient task execution in shared control, it is essential to predict the desired behavior for a given situation or context in order to simplify the control task for the human operator. This prediction is obtained by exploiting Learning from Demonstration (LfD), which is a popular approach for transferring human skills to robots. We encode the demonstrated behavior as trajectory distributions and generalize the learned distributions to new situations. The goal of this paper is to present a shared control framework that uses learned expert distributions to gain more autonomy. Our approach controls the balance between the controller's autonomy and the human preference based on the distributions of the demonstrated trajectories. Moreover, the learned distributions are autonomously refined from collaborative task executions, resulting in a master-slave system with increasing autonomy that requires less user input with an increasing number of task executions. We experimentally validated that our shared control approach enables efficient task executions. Moreover, the conducted experiments demonstrated that the developed system improves its performances through interactive task executions with our shared control.

Nowadays, one of the largest environmental challenges that European countries must face consists in dealing with the past half century of nuclear waste. In order to optimize maintenance costs, nuclear waste must be sorted, segregated and stored according to its radiation level. Towards this end, in [1] we have recently proposed a visual-based shared control architecture meant to facilitate a human operator in controlling two remote robotic arms (one equipped with a gripper and another with a camera) during remote manipulation tasks of nuclear waste via a master device. The operator could then receive force cues informative of the feasibility of her/his motion commands during the task execution. The strategy presented in [1], albeit effective, suffers however from a locality issue since the operator can only provide instantaneous velocity commands (in a suitable task space), and receive instantaneous force feedback cues. On the other hand, the ability to `steer' a whole future trajectory in task space, and to receive a corresponding integral force feedback along the whole planned trajectory (because of any constraint of the considered system), could significantly enhance the operator's performance, especially when dealing with complex manipulation tasks. The aim of this work is to then extend [1] towards a planning-based shared control architecture able to take into account the mentioned requirements. A human/hardware-in-the-loop experiment with simulated slave robots and a real master device is reported for demonstrating the feasibility and effectiveness of the proposed approach.

Cleaning up the past half century of nuclear waste represents the largest environmental remediation project in the whole Europe. Nuclear waste must be sorted, segregated and stored according to its radiation level in order to optimize maintenance costs. The objective of this work is to develop a shared control framework for remote manipulation of objects using visual information. In the presented scenario, the human operator must control a system composed of two robotic arms, one equipped with a gripper and the other one with a camera. In order to facilitate the operator's task, a subset of the gripper motion are assumed to be regulated by an autonomous algorithm exploiting the camera view of the scene. At the same time, the operator has control over the remaining null-space motions w.r.t. the primary (autonomous) task by acting on a force feedback device. A novel force feedback algorithm is also proposed with the aim of informing the user about possible constraints of the robotic system such as, for instance, joint limits. Human/hardware-in-the-loop experiments with simulated slave robots and a real master device are finally reported for demonstrating the feasibility and effectiveness of the approach.

Haptic devices have been used to help people to learn or recover specific movements. An interesting application is the handwriting. In this paper we present a haptic device designed to help people to improve their writing skills. Guided by a teacher, who controls one of the mechanisms providing the haptic feedback, a group of students were asked to write few words using their second hand. The test led to some interesting outcomes. A quantitative analysis is then carried out in order to support the considerations uniquely based on the qualitative observation of the results.