Title: Robotic motion coordination based on a geometric deformation measure
Authors: Miguel Aranda, Jose Sanchez, Juan Antonio Corrales Ramon and Youcef Mezouar
Journal: IEEE Systems Journal, doi: 10.1109/JSYST.2021.3107779
Abstract: This article describes a novel approach to achieve motion coordination in a multirobot system based on the concept of deformation. Our main novel contribution is to link these two elements (namely, coordination and deformation). In particular, the core idea of our approach is that the robots’ motions minimize a global measure of the deformation of their positions relative to a prescribed shape. Based on this idea we propose a linear shape controller, that also incorporates a term modeling an affine deformation. We show that the affine term is particularly useful when the deformation to be controlled is large. We also propose controls for the other variables (centroid, rotation, size) that define the geometric configuration of the team. Importantly, these additional controls are completely decoupled from the shape control. The overall approach is simple and robust, and it creates closely coordinated robot motions. Being based on deformation, it is useful in several scenarios involving manipulation tasks: e.g., handling of a highly deformable object, control of an object’s shape, or regulation of the shape formed by the fingertips of a robotic hand. We present simulation and experimental results to validate the proposed approach.
Title: Collision-free Transport of 2D Deformable Objects
Authors: Rafael Herguedas, Gonzalo Lopez-Nicolas, Carlos Sagues
Conference: International Conference on Control, Automation, and Systems (ICCAS 2021), Jeju, Korea, October 12-15, 2021
Abstract: We propose a novel system to transport 2D cloth-like deformable objects with mobile manipulators and without collisions along a known path. First, a new deformation model that allows for real-time shape prediction, based on the paradigm of deformable bounding box, is presented. The transport task is next defined as an optimization problem, which includes a set of linear and nonlinear constraints. These constraints allow to limit the object’s deformations and rotations and to avoid obstacles, respectively. Simulation results are reported to demonstrate the validity of our method.
Title: Multi-scale Laplacian-based FMM for shape control
Authors: Ignacio Cuiral-Zueco and Gonzalo Lopez-Nicolas
Conference: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). September 27 – October 1, 2021. Prague, Czech Republic
Abstract: Shape control has become a prominent research field as it enables the automation of tasks in many applications. Overall, deforming an object to a desired target shape by using few grippers is a major challenge. The limited information about the object dynamics, the need to combine small and large deformations in order to achieve certain target shapes and the non-linear nature of most deformable objects are factors that significantly hamper shape control performance. In this paper, we propose a shape control method for multi-robot manipulation of large-strain deformable objects. Our approach is based on multi-scale Laplacian descriptors that feed an FMM (Fast Marching Method) for elastic shape contour matching. The FMM’s resulting path and the Laplacian operator are used to define a control strategy for the robot grippers. Simulation experiments carried out with an ARAP (As Rigid As Possible) deformation model provide satisfactory results.
Title: Enclosing a moving target with an optimally rotated and scaled multiagent pattern
Authors: M. Aranda, Y. Mezouar, G. López-Nicolás, C. Sagüés
Journal: International Journal of Control, vol. 94, no. 3, pp. 601-611, 2021
Abstract: We propose a novel control method to enclose a moving target in a two-dimensional setting with a team of agents forming a prescribed geometric pattern. The approach optimises a measure of the overall agent motion costs, via the minimisation of a suitably defined cost function encapsulating the pattern rotation and scaling. We propose two control laws which use global information and make the agents exponentially converge to the prescribed formation with an optimal scale that remains constant, while the team’s centroid tracks the target. One control law results in a multiagent pattern that keeps a constant orientation in the workspace; for the other, the pattern rotates with constant speed. These behaviors, whose optimality and steadiness are very relevant for the task addressed, occur independently from the target’s velocity. Moreover, the methodology does not require distance measurements, common coordinate references, or communications. We also present formal guarantees of collision avoidance for the proposed approach. Illustrative simulation examples are provided.
Title: Distributed Linear Control of Multirobot Formations Organized in Triads
Authors: M. Aranda, G. López-Nicolás and Y. Mezouar
Journal: IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 310-317, Oct. 2021
Abstract: This letter addresses the problem of controlling multiple robots to form a prescribed team shape in two-dimensional space. We consider a team organization in interlaced triads (i.e., groups of three robots). For each triad we define a measure of geometric deformation relative to its prescribed shape. Our main contribution is a novel distributed control law, defined as the gradient descent on the sum of these triangular deformation measures. We show that this geometrically motivated control law is linear, and bears analogies with existing formulations. Moreover, in comparisonwith these formulations our controller is simpler and more flexible to design, converges to the globally optimal shape by construction, and allows analysis of the team size dynamics. We illustrate the proposed approach in simulation.
Title: Towards footwear manufacturing 4.0: shoe sole robotic grasping in assembling operations Author: Guillermo Oliver, Pablo Gil, Jose F. Gomez, Fernando Torres Journal: The International Journal of Advanced Manufacturing Technology, 2021
Abstract: In this paper, we present a robotic workcell for task automation in footwear manufacturing such as sole digitization, glue dispensing, and sole manipulation from different places within the factory plant. We aim to make progress towards shoe industry 4.0. To achieve it, we have implemented a novel sole grasping method, compatible with soles of different shapes, sizes, and materials, by exploiting the particular characteristics of these objects. Our proposal is able to work well with low density point clouds from a single RGBD camera and also with dense point clouds obtained from a laser scanner digitizer. The method computes antipodal grasping points from visual data in both cases and it does not require a previous recognition of sole. It relies on sole contour extraction using concave hulls and measuring the curvature on contour areas. Our method was tested both in a simulated environment and in real conditions of manufacturing at INESCOP facilities, processing 20 soles with different sizes and characteristics. Grasps were performed in two different configurations, obtaining an average score of 97.5% of successful real grasps for soles without heel made with materials of low or medium flexibility. In both cases, the grasping method was tested without carrying out tactile control throughout the task.
Title: 3D reconstruction of deformable objects from RGB-D cameras: an omnidirectional inward-facing multi-camera system Authors: Eva Curto, Helder Araujo Conference: 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP’2021)
Abstract: This is a paper describing a system made up of several inward-facing cameras able to perform reconstruction of deformable objects through synchronous acquisition of RGBD data. The configuration of the camera system allows the acquisition of 3D omnidirectional images of the objects. The paper describes the structure of the system as well as an approach for the extrinsic calibration, which allows the estimation of the coordinate transformations between the cameras. Reconstruction results are also presented. Download paper
Title: Intel RealSense SR305, D415 and L515: Experimental evaluation and comparison of depth estimation Authors: Francisco Lourenco, Helder Araujo Conference: 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP’2021)
Abstract: In the last few years Intel has launched several low cost RGB-D cameras. Three of these cameras are the SR305, the L415 and the L515. These three cameras are based on different operating principles. The SR305 is based on structured light projection, the D415 is based on stereo based using also the projection of random dots and the L515 is based on LIDAR. In addition they all provide RGB images. In this paper we perform and experimental analysis and comparison of the depth estimation by the three cameras. Download paper
Title: RGB-D Sensing of Challenging Deformable Objects
Authors: Ignacio Cuiral-Zueco and Gonzalo Lopez-Nicolas
Workshop: Workshop on Managing deformation: A step towards higher robot autonomy (MaDef), 25 October – 25 December, 2020
Abstract: The problem of deformable object tracking is prominent in recent robot shape-manipulation research. Additionally, texture-less objects that undergo large deformations and movements lead to difficult scenarios. Three RGB-D sequences of different challenging scenarios are processed in order to evaluate the robustness and versatility of a deformable object tracking method. Everyday objects of different complex characteristics are manipulated and tracked. The tracking system, pushed out the comfort zone, performs satisfactorily.
Title: Experimental multi-camera setup for perception of dynamic objects
Authors: Rafael Herguedas, Gonzalo Lopez-Nicolas and Carlos Sagues
Workshop: Robotic Manipulation of Deformable Objects (ROMADO), 25 October – 25 December, 2020
Abstract: Currently, perception and manipulation of dynamic objects represent an open research problem. In this paper, we show a proof of concept of a multi-camera robotic setup which is intended to perform coverage of dynamic objects. The system includes a set of RGB-D cameras, which are positioned and oriented to cover the object’s contour as required in terms of visibility. An algorithm of a previous study allows us to minimize and configure the cameras so that collisions and occlusions are avoided. We test the validity of the platform with the Robot Operating System (ROS) in simulations with the software Gazebo and in real experiments with Intel RealSense modules.