Cable Detection and Manipulation for DLO-in-Hole Assembly Tasks

This paper describes a cyber-physical system for the manipulation of Deformable Linear Objects (DLOs) addressing the DLO-in-hole insertion problem targeting an industrial sce-nario, the switchgear's components cabling task. In particular, the task considered is the insertion of DLOs in the switchgear components' holes. This task is very challenging since a precise knowledge of the DLO tip position and orientation is required for a successful operation. We tackled the DLO-in-hole problem from the computer vision perspective constraining our setup on employing just simple 2D images and by using the mobility of the robotic arm for achieving the full 3D knowledge of the DLOs. Then, the DLO tip is detected from two different image planes and the robot's trajectory corrected accordingly before insertion. To prove the effectiveness of the proposed solution, an example scenario is prepared and the method validated experimentally attempting the insertion of several DLOs in a sample switchgear component, obtaining an overall insertion success rate of 82.5 %

Vision-Based Robotic Solution for Wire Insertion with an Assigned Label Orientation

This paper tackles the problem of wire insertion in switchgear assembly according to the current regulations. In particular, the wire connections require that the wire label has to be oriented facing up in order to simplify and speed up testing and maintenance of the switchgear. The proposed approach exploits the a priori knowledge of the scenario with a calibrated RGB camera and a robotic arm to estimate both wire end pose and label position. The procedure combines several techniques (gradient base, trained classifier and stereo vision) to elaborate standard images in order to extract some wire features related to its shape and label. Specific frames are fixed according to estimated features and then used to correctly complete the task by using a robotic system. Experiments are reported to verify the effectiveness of the proposed approach

Tactile based robotic skills for cable routing operations

This paper proposes a set of tactile based skills to perform robotic cable routing operations for deformable linear objects (DLOs) characterized by considerable stiffness and constrained at both ends. In particular, tactile data are exploited to reconstruct the shape of the grasped portion of the DLO and to estimate the future local one. This information is exploited to obtain a grasping configuration aligned to the local shape of the DLO, starting from a rough initial grasping pose, and to follow the DLO's contour in the three-dimensional space. Taking into account the distance travelled along the arc length of the DLO, the robot can detect the cable segments that must be firmly grasped and inserted in intermediate clips, continuing then to slide along the contour until the next DLO's portion, that has to be clipped, is reached. The proposed skills are experimentally validated with an industrial robot on different DLOs in several configurations and on a cable routing use case

Model-based Manipulation of Deformable Linear Objects by Multivariate Dynamic Splines

In this paper, the modelling and the simulation of a Deformable Linear Object (DLO) manipulation are reported. The main motivation of this study is to define a strategy to enable a robotic manipulator to predict in real time the shape a DLO will achieve during the execution of a manipulation action. To accomplish this target in a reasonable time, according to the possibility of adopting this solution in an industrial manufacturing system, an approximate but physically consistent model of the DLO is adopted considering the predominant plasticity of the object to be manipulated, as in the case of electric cable manipulation. The DLO manipulation model is based on multivariate dynamic splines solved iteratively in real-time to interpolate the DLO shape during the manipulation sequence. The systems assumes to be able to detect the initial configuration of the DLO at each iteration of the algorithm by means of a proper vision system. Preliminary simulation results are presented to show the effectiveness of the method

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Study of robotics systems applications to the space station program

Applications of robotics systems to potential uses of the Space Station as an assembly facility, and secondarily as a servicing facility, are considered. A typical robotics system mission is described along with the pertinent application guidelines and Space Station environmental assumptions utilized in developing the robotic task scenarios. A functional description of a supervised dual-robot space structure construction system is given, and four key areas of robotic technology are defined, described, and assessed. Alternate technologies for implementing the more routine space technology support subsystems that will be required to support the Space Station robotic systems in assembly and servicing tasks are briefly discussed. The environmental conditions impacting on the robotic configuration design and operation are reviewed

Robot Learning-Based Pipeline for Autonomous Reshaping of a Deformable Linear Object in Cluttered Backgrounds

open2noThis work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program as part of RIA Project Robotic tEchnologies for the Manipulation of cOmplex DeformablE Linear objects (REMODEL) under Grant 870133.In this work, the robotic manipulation of a highly Deformable Linear Object (DLO) is addressed by means of a sequence of pick-and-drop primitives driven by visual data. A decision making process learns the optimal grasping location exploiting deep Q-learning and finds the best releasing point from a path representation of the DLO shape. The system effectively combines a state-of-the-art algorithm for semantic segmentation specifically designed for DLOs with deep reinforcement learning. Experimental results show that our system is capable to manipulate a DLO into a variety of different shapes in few steps. The intermediate steps of deformation that lead the object from its initial configuration to the target one are also provided and analyzed.openZanella R.; Palli G.Zanella R.; Palli G