Co-manipulation of soft-materials estimating deformation from depth images

Human-robot co-manipulation of soft materials, such as fabrics, composites, and sheets of paper/cardboard, is a challenging operation that presents several relevant industrial applications. Estimating the deformation state of the co-manipulated material is one of the main challenges. Viable methods provide the indirect measure by calculating the human-robot relative distance. In this paper, we develop a data-driven model to estimate the deformation state of the material from a depth image through a Convolutional Neural Network (CNN). First, we define the deformation state of the material as the relative roto-translation from the current robot pose and a human grasping position. The model estimates the current deformation state through a Convolutional Neural Network, specifically a DenseNet-121 pretrained on ImageNet.The delta between the current and the desired deformation state is fed to the robot controller that outputs twist commands. The paper describes the developed approach to acquire, preprocess the dataset and train the model. The model is compared with the current state-of-the-art method based on a skeletal tracker from cameras. Results show that our approach achieves better performances and avoids the various drawbacks caused by using a skeletal tracker.Finally, we also studied the model performance according to different architectures and dataset dimensions to minimize the time required for dataset acquisitionComment: Pre-print, submitted to Journal of Intelligent Manufacturin

On the use of a temperature based friction model for a virtual force sensor in industrial robot manipulators

In this paper we propose the use of a dynamic model in which the effects of temperature on friction are considered to develop a virtual force sensor for industrial robot manipulators. The estimation of the inertial parameters and of the friction model are explained. The effectiveness of the virtual force sensor has been proven in a polishing task. In fact, the interaction forces between the robot and the environment has been measured both with the virtual force sensor and a common load cell. Moreover, the advantages provided by considering the temperature dependency are highlighted

Robotic assistance for industrial sanding with a smooth approach to the surface and boundary constraints

Surface treatment operations, such as sanding, deburring, finishing, grinding, polishing, etc. are progressively becoming more automated using robotic systems. However, previous research in this field used a completely automatic operation of the robot system or considered a low degree of human-robot interaction. Therefore, to overcome this issue, this work develops a truly synergistic cooperation between the human operator and the robot system to get the best from both. In particular, in the application developed in this work the human operator provides flexibility, guiding the tool of the robot system to treat arbitrary regions of the workpiece surface; while the robot system provides strength, accuracy and security, not only holding the tool and keeping the right tool orientation, but also guaranteeing a smooth approach to the workpiece and confining the tool within the allowed area close to the workpiece. Moreover, to add more flexibility to the proposed method, when the user is not guiding the robot tool, a robot automatic operation is activated to perform the treatment in prior established regions. Furthermore, a camera network is used to get a global view of the robot workspace in order to obtain the workpiece location accurately and in real-time. The effectiveness of the proposed approach is shown with several experiments using a 6R robotic arm

From Human Perception and Action Recognition to Causal Understanding of Human-Robot Interaction in Industrial Environments

Human-robot collaboration is migrating from lightweight robots in laboratory environments to industrial applications, where heavy tasks and powerful robots are more common. In this scenario, a reliable perception of the humans involved in the process and related intentions and behaviors is fundamental. This paper presents two projects investigating the use of robots in relevant industrial scenarios, providing an overview of how industrial human-robot collaborative tasks can be successfully addressed

Robots in machining

Robotic machining centers offer diverse advantages: large operation reach with large reorientation capability, and a low cost, to name a few. Many challenges have slowed down the adoption or sometimes inhibited the use of robots for machining tasks. This paper deals with the current usage and status of robots in machining, as well as the necessary modelling and identification for enabling optimization, process planning and process control. Recent research addressing deburring, milling, incremental forming, polishing or thin wall machining is presented. We discuss various processes in which robots need to deal with significant process forces while fulfilling their machining task

Optimizing parameters of robotic task-oriented programming via a multiphysics simulation

The programming complexity of industrial robots significantly limits their expansion in complex industrial applications. Consequently, research has focused extensively on the development of intuitive programming methods.This article proposes a framework for task-oriented programming introducing an intuitive and modular task structure. The framework provides an algorithm able to optimize the execution parameter of the tasks. A physical simulation environment allows accurate parameter optimization in a virtual environment providing feasible and safe results. Efficiency tests demonstrated the method's effectiveness, and a comparison with genetic and Bayesian -based ones have been conducted

Simplify the robot programming through an action-and-skill manipulation framework

The paper introduces a robotic manipulation framework suitable for the execution of manipulation tasks. Based on the ROS platform, the framework provides advanced motion planning and control functionalities for robotic systems to guarantee a high level of autonomy during the execution of an action. The integrated motion planning module can handle multiple motion planners to generate collision-free trajectories for a given planning scene that can be dynamically uploaded. In the same way, the robot controllers can be changed online on the base of the robot behavior required by the action under execution. The motion control of the robotic system is fully demanded to the manipulation framework relieving the upper control layers from the management of low-level functionalities and the task geometrical information. The framework can be used downstream to a task planner or as a standalone library to simplify the robot programming in complex manipulation tasks

A general analytical procedure for robot dynamic model reduction

The identification of the dynamic model of a robotic manipulator represents a fundamental step for designing high performance model-based controllers. Despite the huge number of works presented on this topic, the symbolic dynamic model reduction (i.e., the identification of the set of parameters observable through the measure of joint torques and positions) still remain a challenging task, characterized from tailored solutions, adapted from time to time to specific families of mechanisms. The work here presented, introduces an automatic and analytical reduction of the dynamic model, based on a multi-dimensional Fourier series decomposition of the dynamic equations. The procedure enables to obtain symbolically the base dynamic parameters (BP) starting from a given kinematic structure. The Fourier based model reduction can be applied indifferently both to open- and closed-chain kinematics. A simulated example shows the effectiveness of the proposed algorithm