arXiv subjects

David Navarro-Alarcon

Publications and source records attributed to David Navarro-Alarcon.

At least 55 records · Page 3Linked to original sources

arXiv2021

Challenges and Outlook in Robotic Manipulation of Deformable Objects

Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and healthcare sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex, and is still an open research problem. Addressing DOM challenges demand breakthroughs in almost all aspects of robotics, namely hardware design, sensing, (deformation) modeling, planning, and control. In this article, we review recent advances and highlight the main challenges when considering deformation in each sub-field. A particular focus of our paper lies in the discussions of these challenges and proposing future directions of research.

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arXiv2021

LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation

Soft object manipulation has recently gained popularity within the robotics community due to its potential applications in many economically important areas. Although great progress has been recently achieved in these types of tasks, most state-of-the-art methods are case-specific; They can only be used to perform a single deformation task (e.g. bending), as their shape representation algorithms typically rely on "hard-coded" features. In this paper, we present LaSeSOM, a new feedback latent representation framework for semantic soft object manipulation. Our new method introduces internal latent representation layers between low-level geometric feature extraction and high-level semantic shape analysis; This allows the identification of each compressed semantic function and the formation of a valid shape classifier from different feature extraction levels. The proposed latent framework makes soft object representation more generic (independent from the object's geometry and its mechanical properties) and scalable (it can work with 1D/2D/3D tasks). Its high-level semantic layer enables to perform (quasi) shape planning tasks with soft objects, a valuable and underexplored capability in many soft manipulation tasks. To validate this new methodology, we report a detailed experimental study with robotic manipulators.

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arXiv2021

Keypoint-Based Bimanual Shaping of Deformable Linear Objects under Environmental Constraints using Hierarchical Action Planning

This paper addresses the problem of contact-based manipulation of deformable linear objects (DLOs) towards desired shapes with a dual-arm robotic system. To alleviate the burden of high-dimensional continuous state-action spaces, we model the DLO as a kinematic multibody system via our proposed keypoint detection network. This new perception network is trained on a synthetic labeled image dataset and transferred to real manipulation scenarios without conducting any manual annotations. Our goal-conditioned policy can efficiently learn to rearrange the configuration of the DLO based on the detected keypoints. The proposed hierarchical action framework tackles the manipulation problem in a coarse-to-fine manner (with high-level task planning and low-level motion control) by leveraging on two action primitives. The identification of deformation properties is avoided since the algorithm replans its motion after each bimanual execution. The conducted experimental results reveal that our method achieves high performance in state representation of the DLO, and is robust to uncertain environmental constraints.

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arXiv2021

Learning Cloth Folding Tasks with Refined Flow Based Spatio-Temporal Graphs

Cloth folding is a widespread domestic task that is seemingly performed by humans but which is highly challenging for autonomous robots to execute due to the highly deformable nature of textiles; It is hard to engineer and learn manipulation pipelines to efficiently execute it. In this paper, we propose a new solution for robotic cloth folding (using a standard folding board) via learning from demonstrations. Our demonstration video encoding is based on a high-level abstraction, namely, a refined optical flow-based spatiotemporal graph, as opposed to a low-level encoding such as image pixels. By constructing a new spatiotemporal graph with an advanced visual corresponding descriptor, the policy learning can focus on key points and relations with a 3D spatial configuration, which allows to quickly generalize across different environments. To further boost the policy searching, we combine optical flow and static motion saliency maps to discriminate the dominant motions for better handling the system dynamics in real-time, which aligns with the attentional motion mechanism that dominates the human imitation process. To validate the proposed approach, we analyze the manual folding procedure and developed a custom-made end-effector to efficiently interact with the folding board. Multiple experiments on a real robotic platform were conducted to validate the effectiveness and robustness of the proposed method.

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arXiv2021

Follow the Curve: Robotic-Ultrasound Navigation with Learning Based Localization of Spinous Processes for Scoliosis Assessment

The scoliosis progression in adolescents requires close monitoring to timely take treatment measures. Ultrasound imaging is a radiation-free and low-cost alternative in scoliosis assessment to X-rays, which are typically used in clinical practice. However, ultrasound images are prone to speckle noises, making it challenging for sonographers to detect bony features and follow the spine's curvature. This paper introduces a robotic-ultrasound approach for spinal curvature tracking and automatic navigation. A fully connected network with deconvolutional heads is developed to locate the spinous process efficiently with real-time ultrasound images. We use this machine learning-based method to guide the motion of the robot-held ultrasound probe and follow the spinal curvature while capturing ultrasound images and correspondent position. We developed a new force-driven controller that automatically adjusts the probe's pose relative to the skin surface to ensure a good acoustic coupling between the probe and skin. After the scanning, the acquired data is used to reconstruct the coronal spinal image, where the deformity of the scoliosis spine can be assessed and measured. To evaluate the performance of our methodology, we conducted an experimental study with human subjects where the deviations from the image center during the robotized procedure are compared to that obtained from manual scanning. The angles of spinal deformity measured on spinal reconstruction images were similar for both methods, implying that they equally reflect human anatomy.

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arXiv2021

Towards a Multispectral RGB-IR-UV-D Vision System -- Seeing the Invisible in 3D

In this paper, we present the development of a sensing system with the capability to compute multispectral point clouds in real-time. The proposed multi-eye sensor system effectively registers information from the visible, (long-wave) infrared, and ultraviolet spectrum to its depth sensing frame, thus enabling to measure a wider range of surface features that are otherwise hidden to the naked eye. For that, we designed a new cross-calibration apparatus that produces consistent features which can be sensed by each of the cameras, therefore, acting as a multispectral "chessboard". The performance of the sensor is evaluated with two different cases of studies, where we show that the proposed system can detect "hidden" features of a 3D environment.

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arXiv2021

Can a Tesla Turbine be Utilised as a Non-Magnetic Actuator for MRI-Guided Robotic Interventions?

This paper introduces a new type of nonmagnetic actuator for MRI interventions. Ultrasonic and piezoelectric motors are one the most commonly used actuators in MRI applications. However, most of these actuators are only MRI-safe, which means they cannot be operated while imaging as they cause significant visual artifacts. To cope with this issue, we developed a new pneumatic rotary servo-motor (based on the Tesla turbine) that can be effectively used during continuous MR imaging. We thoroughly tested the performance and magnetic properties of our MRI-compatible actuator with several experiments, both inside and outside an MRI scanner. The reported results confirm the feasibility to use this motor for MRI-guided robotic interventions.

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arXiv2021

A Novel Approach to Model the Kinematics of Human Fingers Based on an Elliptic Multi-Joint Configuration

In this paper, we present a novel kinematic model of the human phalanges based on the elliptical motion of their joints. The presence of the soft elastic tissues and the general anatomical structure of the hand joints highly affect the relative movement of the bones. Commonly used assumption of circular trajectories simplifies the designing process but leads to divergence with the actual hand behavior. The advantages of the proposed model are demonstrated through the comparison with the conventional revolute joint model. Conducted simulations and experiments validate designed forward and inverse kinematic algorithms. Obtained results show a high performance of the model in mimicking the human fingertip motion trajectory.

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arXiv2021

A Split-face Study of Novel Robotic Prototype vs Human Operator in Skin Rejuvenation Using Q-switched Nd:Yag Laser: Accuracy, Efficacy and Safety

Background: Robotic technologies involved in skin laser are emerging. Objective: To compare the accuracy, efficacy and safety of novel robotic prototype with human operator in laser operation performance for skin photo-rejuvenation. Methods: Seventeen subjects were enrolled in a prospective, comparative split-face trial. Q-switch 1064nm laser conducted by the robotic prototype was provided on the right side of the face and that by the professional practitioner on the left. Each subject underwent a single time, one-pass, non-overlapped treatment on an equal size area of the forehead and cheek. Objective assessments included: treatment duration, laser irradiation shots, laser coverage percentage, VISIA parameters, skin temperature and the VAS pain scale. Results: Average time taken by robotic manipulator was longer than human operator; the average number of irradiation shots of both sides had no significant differences. Laser coverage rate of robotic manipulator (60.2 +-15.1%) was greater than that of human operator (43.6 +-12.9%). The VISIA parameters showed no significant differences between robotic manipulator and human operator. No short or long-term side effects were observed with maximum VAS score of 1 point. Limitations: Only one section of laser treatment was performed. Conclusion: Laser operation by novel robotic prototype is more reliable, stable and accurate than human operation.

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arXiv2021

Contour Moments Based Manipulation of Composite Rigid-Deformable Objects with Finite Time Model Estimation and Shape/Position Control

The robotic manipulation of composite rigid-deformable objects (i.e. those with mixed non-homogeneous stiffness properties) is a challenging problem with clear practical applications that, despite the recent progress in the field, it has not been sufficiently studied in the literature. To deal with this issue, in this paper we propose a new visual servoing method that has the capability to manipulate this broad class of objects (which varies from soft to rigid) with the same adaptive strategy. To quantify the object's infinite-dimensional configuration, our new approach computes a compact feedback vector of 2D contour moments features. A sliding mode control scheme is then designed to simultaneously ensure the finite-time convergence of both the feedback shape error and the model estimation error. The stability of the proposed framework (including the boundedness of all the signals) is rigorously proved with Lyapunov theory. Detailed simulations and experiments are presented to validate the effectiveness of the proposed approach. To the best of the author's knowledge, this is the first time that contour moments along with finite-time control have been used to solve this difficult manipulation problem.

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arXiv2021

Vision-based Manipulation of Deformable and Rigid Objects Using Subspace Projections of 2D Contours

This paper proposes a unified vision-based manipulation framework using image contours of deformable/rigid objects. Instead of using human-defined cues, the robot automatically learns the features from processed vision data. Our method simultaneously generates -- from the same data -- both, visual features and the interaction matrix that relates them to the robot control inputs. Extraction of the feature vector and control commands is done online and adaptively, with little data for initialization. The method allows the robot to manipulate an object without knowing whether it is rigid or deformable. To validate our approach, we conduct numerical simulations and experiments with both deformable and rigid objects.

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arXiv2021

Towards Latent Space Based Manipulation of Elastic Rods using Autoencoder Models and Robust Centerline Extractions

The automatic shape control of deformable objects is a challenging (and currently hot) manipulation problem due to their high-dimensional geometric features and complex physical properties. In this study, a new methodology to manipulate elastic rods automatically into 2D desired shapes is presented. An efficient vision-based controller that uses a deep autoencoder network is designed to compute a compact representation of the object's infinite-dimensional shape. An online algorithm that approximates the sensorimotor mapping between the robot's configuration and the object's shape features is used to deal with the latter's (typically unknown) mechanical properties. The proposed approach computes the rod's centerline from raw visual data in real-time by introducing an adaptive algorithm on the basis of a self-organizing network. Its effectiveness is thoroughly validated with simulations and experiments.

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arXiv2021

Bio-Inspired Design of Artificial Striated Muscles Composed of Sarcomere-Like Contraction Units (preprint)

Biological muscles have always attracted robotics researchers due to their efficient capabilities in compliance, force generation, and mechanical work. Many groups are working on the development of artificial muscles, however, state-of-the-art methods still fall short in performance when compared with their biological counterpart. Muscles with high force output are mostly rigid, whereas traditional soft actuators take much space and are limited in strength and producing displacement. In this work, we aim to find a reasonable trade-off between these features by mimicking the striated structure of skeletal muscles. For that, we designed an artificial pneumatic myofibril composed of multiple contraction units that combine stretchable and inextensible materials. Varying the geometric parameters and the number of units in series provides flexible adjustment of the desired muscle operation. We derived a mathematical model that predicts the relationship between the input pneumatic pressure and the generated output force. A detailed experimental study is conducted to validate the performance of the proposed bio-inspired muscle.

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arXiv2021

A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-based Manipulation Tasks

While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behaviour. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepancies from the reference values. While most studies consider providing the teaching signal as error in joint-space, few studies consider the error in task-space and even fewer consider the spiking nature of the cerebellum on the cellular-level. In this study, a detailed cellular-level forward cerebellar model is developed, including modeling of Golgi and Basket cells which are usually neglected in previous studies. To preserve the biological features of the cerebellum in the developed model, a hyperparameter optimization method tunes the network accordingly. The efficiency and biological plausibility of the proposed cerebellar-based controller is then demonstrated under different robotic manipulation tasks reproducing motor behaviour observed in human reaching experiments.

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arXiv2021

Shape Control of Elastic Objects Based on Implicit Sensorimotor Models and Data-Driven Geometric Features

This paper proposes a general approach to design automatic controls to manipulate elastic objects into desired shapes. The object's geometric model is defined as the shape feature based on the specific task to globally describe the deformation. Raw visual feedback data is processed using classic regression methods to identify parameters of data-driven geometric models in real-time. Our proposed method is able to analytically compute a pose-shape Jacobian matrix based on implicit functions. This model is then used to derive a shape servoing controller. To validate the proposed method, we report a detailed experimental study with robotic manipulators deforming an elastic rod.

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arXiv2020

On Radiation-Based Thermal Servoing: New Models, Controls and Experiments

In this paper, we introduce a new sensor-based control method that regulates (by means of robot motions) the heat transfer between a radiative source and an object of interest. This valuable sensorimotor capability is needed in many industrial, dermatology and field robot applications, and it is an essential component for creating machines with advanced thermo-motor intelligence. To this end, we derive a geometric-thermal-motor model which describes the relationship between the robot's active configuration and the produced dynamic thermal response. We then use the model to guide the design of two new thermal servoing controllers (one model-based and one adaptive), and analyze their stability with Lyapunov theory. To validate our method, we report a detailed experimental study with a robotic manipulator conducting autonomous thermal servoing tasks. To the best of the authors' knowledge, this is the first time that temperature regulation has been formulated as a motion control problem for robots.

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arXiv2020

Path Planning with Automatic Seam Extraction over Point Cloud Models for Robotic Arc Welding

This paper presents a point cloud based robotic system for arc welding. Using hand gesture controls, the system scans partial point cloud views of workpiece and reconstructs them into a complete 3D model by a linear iterative closest point algorithm. Then, a bilateral filter is extended to denoise the workpiece model and preserve important geometrical information. To extract the welding seam from the model, a novel intensity-based algorithm is proposed that detects edge points and generates a smooth 6-DOF welding path. The methods are tested on multiple workpieces with different joint types and poses. Experimental results prove the robustness and efficiency of this robotic system on automatic path planning for welding applications.

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arXiv2020

Robotics Meets Cosmetic Dermatology: Development of a Novel Vision-Guided System for Skin Photo-Rejuvenation

In this paper, we present a novel robotic system for skin photo-rejuvenation procedures, which can uniformly deliver the laser's energy over the skin of the face. The robotised procedure is performed by a manipulator whose end-effector is instrumented with a depth sensor, a thermal camera, and a cosmetic laser generator. To plan the heat stimulating trajectories for the laser, the system computes the surface model of the face and segments it into seven regions that are automatically filled with laser shots. We report experimental results with human subjects to validate the performance of the system. To the best of the author's knowledge, this is the first time that facial skin rejuvenation has been automated by robot manipulators.

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