3D Robot Research Articles

Navigated and minimally invasive screw osteosynthesis of atalus fracture

The aim of this surgery is to safeguard the multifragmentary and nondisplaced talus fracture (body and neck) against secondary dislocation in anavigated and minimally invasive manner using screw osteosynthesis. Due to the young age of the patient in the presented case and the risk of apossible secondary dislocation, the decision was made in favor of surgical treatment. Soft tissue swelling, wound infections and allergies to the osteosynthesis material. The video is available online (in English) and shows the individual surgical steps in detail. Preoperative computed tomography (CT) imaging and screw planning. Attachment of the reference array. 1) Cone beam CT (CBCT) scan, image fusion and fusion control. Planning of the minimally invasive skin incisions. Skin incision, navigated drilling and insertion of the K‑wires. 2) CBCT scan and position check of the K‑wires, fine adjustment if necessary. Insertion of the screws. 3) CBCT scan with subsequent position check of the screws, retightening of the screws if necessary. Performed in the Robotic Suite (Brainlab, Munich, Germany) using the following elements: navigation unit curve navigation system, movable robotic 3D CBCT, "Loop-X" and wall monitor "BUZZ". Postoperative X‑ray and CT to control the position of the implants. Partial weight-bearing of the foot with 10 kg sole contact for 6weeks. Physiotherapy with active and passive joint mobilization. Thrombosis prophylaxis with enoxaparin sodium. Optional implant removal after approximately 1year. Navigated operations are routine, so far mainly in the area of the spine. This article shows that navigated extremity surgery can be successfully performed in hybrid operating theaters.

Deep Neural Networks for Accurate Depth Estimation with Latent Space Features

Depth estimation plays a pivotal role in advancing human–robot interactions, especially in indoor environments where accurate 3D scene reconstruction is essential for tasks like navigation and object handling. Monocular depth estimation, which relies on a single RGB camera, offers a more affordable solution compared to traditional methods that use stereo cameras or LiDAR. However, despite recent progress, many monocular approaches struggle with accurately defining depth boundaries, leading to less precise reconstructions. In response to these challenges, this study introduces a novel depth estimation framework that leverages latent space features within a deep convolutional neural network to enhance the precision of monocular depth maps. The proposed model features dual encoder–decoder architecture, enabling both color-to-depth and depth-to-depth transformations. This structure allows for refined depth estimation through latent space encoding. To further improve the accuracy of depth boundaries and local features, a new loss function is introduced. This function combines latent loss with gradient loss, helping the model maintain the integrity of depth boundaries. The framework is thoroughly tested using the NYU Depth V2 dataset, where it sets a new benchmark, particularly excelling in complex indoor scenarios. The results clearly show that this approach effectively reduces depth ambiguities and blurring, making it a promising solution for applications in human–robot interaction and 3D scene reconstruction.

Numerical and experimental study of the consolidation of continuous carbon fiber thermoplastics made by robotic 3D printing

Numerical and experimental study of the consolidation of continuous carbon fiber thermoplastics made by robotic 3D printing

Probing the Interplay of Protein Self-Assembly and Covalent Bond Formation in Photo-Crosslinked Silk Fibroin Hydrogels.

Covalent crosslinking of silk fibroin via native tyrosine residues has been extensively explored; however, while these materials are very promising for biomedical, optical, soft robotics, and sensor applications, their structure and mechanical properties are unstable over time. This instability results in spontaneous silk self-assembly and stiffening over time, a process that is poorly understood. This study investigates the interplay between self-assembly and di-tyrosine bond formation in silk hydrogels photo-crosslinked using ruthenium (Ru) and sodium persulfate (SPS) with visible light. The effects of silk concentration, molecular weight, Ru/SPS concentration, and solvent conditions are examined. The Ru/SPS system enables rapid crosslinking, achieving gelation within seconds and incorporating over 90% of silk into the network, even at very low protein concentrations (≥0.75%wt/v). A model emerges where silk self-assembly both before and after crosslinking affects protein phase separation, mesoscale structure, and dynamic changes in the hydrogel network over time. Silk concentration has the greatest impact on hydrogel properties, with higher silk concentration hydrogels experiencing two orders of magnitude increase in stiffness within 1 week. This new understanding and ability to tune hydrogel properties and dynamic stiffening aids in developing advanced materials for 4D biofabrication, sensing, 3D cancer models, drug delivery, and soft robotics.

Biobased coatings for architectural timber applied using the robotic 3D printing technique

Materials applied for coating architectural timber are often environmentally harmful. This study examines a sustainable alternative – a coating from microfibrillated cellulose hydrogel applied for the first time on architectural timber using robotic 3D printing. The proposed solution is evaluated through architectural design and robotic 3D printing experiments, with patterned coating designs deposed onto eight architectural mockups. Through qualitative and quantitative analyses of coating features preproduction and postproduction, fundamental aspects affecting coating design are identified, namely, the 3D printing path geometry and layout, substrate type, and precoating material. These aspects are correlated with the final architectural coating qualities at the mesoscale and macroscale by characterizing dimensional stability, geometric features, and color appearance. The best coating effects are observed for pine substrates precoated with hydroxyethyl cellulose. By delivering new knowledge on biobased architectural coatings, this study contributes to the global effort of phasing out fossil-based materials in the built environment.

Research on 3D humanoid robot pose estimation based on HRNet-Epipolar and CRF robot model by multiple view

Purpose This paper aims to present an unmarked method including entire two-dimensional (2D) and three-dimensional (3D) methods to recover absolute 3D humanoid robot poses from multiview images. Design/methodology/approach The method consists of two separate steps: estimating the 2D poses in multiview images and recovering the 3D poses from the multiview 2D heatmaps. The 2D one is conducted by High-Resolution Net with Epipolar (HRNet-Epipolar), and the Conditional Random Fields Humanoid Robot Pictorial Structure Model (CRF Robot Model) is proposed to recover 3D poses. Findings The performance of the algorithm is validated by experiments developed on data sets captured by four RGB cameras in Qualisys system. It illustrates that the algorithm has higher Mean Per Joint Position Error than Direct Linear Transformation and Recursive Pictorial Structure Model algorithms when estimating 14 joints of the humanoid robot. Originality/value A new unmarked method is proposed for 3D humanoid robot pose estimation. Experimental results show enhanced absolute accuracy, which holds important theoretical significance and application value for humanoid robot pose estimation and motion performance testing.

Development of a Robot-Assisted Fused Deposition Modeling Process for Enhanced Additive Manufacturing

<div>This work aims to define a novel integration of 6 DOF robots with an extrusion-based 3D printing framework that strengthens the possibility of implementing control and simulation of the system in multiple degrees of freedom. Polylactic acid (PLA) is used as an extrusion material for testing, which is a thermoplastic that is biodegradable and is derived from natural lactic acid found in corn, maize, and the like. To execute the proposed framework a virtual working station for the robot was created in RoboDK. RoboDK interprets G-code from the slicing (Slic3r) software. Further analysis and experiments were performed by FANUC 2000ia 165F Industrial Robot. Different tests were performed to check the dimensional accuracy of the parts (rectangle and cylindrical). When the robot operated at 20% of its maximum speed, a bulginess was observed in the cylindrical part, causing the radius to increase from 1 cm to 1.27 cm and resulting in a thickness variation of 0.27 cm at the bulginess location. However, after optimizing the speed at 35% of its maximum speed, 100% dimensional accuracy was achieved. The integration resulted in collision-free robot and extrusion movement, flexibility, capability of making large parts, and enhanced dimensional accuracy.</div>

A novel mapping method for equivalent stiffness and equivalent damping of [formula omitted]-DOF series hydraulic robot

Impedance control is a commonly used approach for robots to interact with environments. At present, the impedance control is mostly implemented at the foot end, while the impedance control at the joint space offers faster response speed and is a more favorable option. However, due to the influence of installation position and moment arm of hydraulic drive unit (HDU), it is more complicated to establish the mapping relationship of equivalent stiffness and equivalent damping between the foot end and joint HDU. To address this challenge, this paper provides a general calculation expression of mapping matrices between the foot end and joint HDU, which also considers the influence of above nonlinear factors. Additionally, the accuracy of mapping matrices is verified through the impedance control, and the “finite difference method” (FDM) is used to enhance the accuracy of validation. Finally, the accuracy of mapping matrices is validated through the single leg of a 2-DOF series hydraulic robot.

3D Pose Nowcasting: Forecast the future to improve the present

Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.

Simulation of a serial topology robot operation using the 3DEXPERIENCE platform

The paper presents the assembling of a 3D model serial topology robot, the definition of kinematical joints of the guiding device mechanism and the simulation of robot operation, using the 3DEXPERIENCE platform. In the end, there are presented different positions of the robot during simulation.

Suitability of UR5 Robot for Robotic 3D Printing

The present paper describes the measurement of the drift of unidirectional pose accuracy, repeatability, and static compliance of a collaborative robot employing a measurement methodology that relies on the description of a virtual ISO cube placed in the robot’s workspace. The measurements aimed to investigate and assess the suitability of the UR5 six-axis collaborative robot for its application in robotic 3D printing. An experimental laboratory measurement workstation was constructed to perform the measurements, and control measurements were performed. The measurements involved describing the TCP point of the robot tool at five measurement points located in a virtual ISO cube during a minimum of 30 repeated measurement cycles. A camera and six linear incremental sensors with assessment units were used for the measurements. The measurements were performed in compliance with the regulations of STN ISO 9283 standard for this type of measurement. As a result of the measurements, the technical specifications of the drift and static compliance of the controlled robotic arm were verified, and the results were compared with the values specified by the manufacturer. Following the measurements and assessment of the results, it was possible to assess the suitability of the used UR5 robotic arm for its application in robotic 3D printing and to propose possible recommendations for the calibration of the robot and the process settings of the printing system for the production of objects using FDM technology.

A Force Control Method Integrating Human Skills for Complex Surface Finishing

Force control is one of the core modules for surface finishing such as grinding, polishing and sanding. However, the current force control methods based on human skills lack in-depth analysis of data patterns or are only applicable to flat surfaces. In addition, surface finishing is mainly performed by hand, resulting in low processing efficiency and poor product consistency. Therefore, this paper proposes a force control method that incorporates human skills to achieve relatively accurate force skill transfer and complex surface finishing. Firstly, human skills consisting of the force skill and the motion skill are learned. The force skill is used to generate the desired force. Then, a series of discrete poses are obtained based on human demonstration and combined with the motion skill to generate the desired trajectory. Finally, a computed-torque impedance control method is proposed to achieve relatively accurate force skill transfer and complex surface finishing by incorporating the desired trajectory and the desired force. The experiments are conducted on a platform composed of a 7-DOF collaborative robot manipulator from Franka Emika and a complex violin surface. The results demonstrate that the proposed force control method can achieve relatively accurate force skill transfer and improve the surface quality of the workpiece.

Adaptive Fault-Tolerant Tracking Control for Multi-Joint Robot Manipulators via Neural Network-Based Synchronization.

In this paper, adaptive fault-tolerant control for multi-joint robot manipulators is proposed through the combination of synchronous techniques and neural networks. By using a synchronization technique, the position error at each joint simultaneously approaches zero during convergence due to the constraints imposed by the synchronization controller. This aspect is particularly important in fault-tolerant control, as it enables the robot to rapidly and effectively reduce the impact of faults, ensuring the performance of the robot when faults occur. Additionally, the neural network technique is used to compensate for uncertainty, disturbances, and faults in the system via online updating. Firstly, novel robust synchronous control for a robot manipulator based on terminal sliding mode control is presented. Subsequently, a combination of the novel synchronous control and neural network is proposed to enhance the fault tolerance of the robot manipulator. Finally, simulation results for a 3-DOF robot manipulator are presented to demonstrate the effectiveness of the proposed controller in comparison to traditional control techniques.

A novel real-time tension distribution method for cable-driven parallel robots

Abstract The tension distribution problem of cable-driven parallel robots is inevitable in real-time control. Currently, iterative algorithms or geometric algorithms are commonly used to solve this problem. Iterative algorithms are difficult to improve in real-time performance, and the tension obtained by geometric algorithms may not be continuous. In this paper, a novel tension distribution method for four-cable, 3-DOF cable-driven parallel robots is proposed based on the wave equation. The tension calculated by this method is continuous and differentiable, without the need for iterative computation or geometric centroid calculations, thus exhibiting good real-time performance. Furthermore, the feasibility and rationality of this algorithm are theoretically proven. Finally, the real-time performance and continuity of cable tension are analyzed through a specific numerical example.

6-DOFs Robot Placement Based on the Multi-Criteria Procedure for Industrial Applications

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.

Joint torque estimation method of industrial robot by combining error compensation model based on LSTM and iterative weighted parameter identification

It is essential for human-robot interaction to accurate joint torque value estimation of industrial robots without force/torque (F/T) sensors. The most common method to estimate the value of robot joint torque is based on dynamic model parameter identification. However, no matter how elaborate the modeling methods are used, there are always uncertainty errors when robot’s joint rotation direction changes. In this paper, an identification framework is proposed to estimate optimal model parameters, and a deep learning network is proposed to compensate for the uncertainty errors caused by the unmodeled dynamics part. At first, the dynamic parameters are estimated during the Least Squares (LS) identification process considering the noise influence and data outliers, and the nonlinear friction model is unified into the identification framework. Secondly, based on Long-Short Term Memory (LSTM) network, an error compensation model (ECM) is proposed to establish the mapping relationship between the joint motion and the identification errors. Finally, a 6-DOF robot is used for parameter identification and ECM validation. Experimental results show that the proposed identification method is better than the LS method. Compared with the torque value estimated by the identification method, the proposed ECM can compensate for the uncertainty errors and improve the torque estimation accuracy.

Robotic arm, 3D vision and geometric matching for an efficient implementation of inverse Patch Transfer Function method

The inverse patch transfer function method was developed to reconstruct the acoustic fields (velocity, pressure, intensity) at the surface of a vibrating source with a complex geometry using the concept of a virtual acoustic domain. This method can be used to manage sources with complex shapes in a non-anechoic acoustic environment, even in the presence of masking objects. However, this method is quite demanding experimentally as it requires a large number of measurement positions with good spatial positioning accuracy. This article presents a dedicated experimental platform developed to simplify and automate the entire measurement process. First, a 3D camera placed on a robotic arm constructs a point cloud characterising the source and its environment. Using a geometric matching algorithm, the CAD of the object under test is detected in the cloud of points, enabling the different coordinate systems (CAD, robot and physical part) to be matched. Secondly, the measurement points required to apply iPTF can be defined directly in the CAD and reproduced in near-real time on the test rig, regardless of the type of antenna used, providing an efficient means of applying iPTF and opening the way to many advanced applications.

Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts

The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a lack of flexibility to adapt to different tasks or types of objects, necessitating extensive adjustments each time a change is required. This highlights the importance of developing a system that can be easily reused and reconfigured to address these challenges. This paper introduces a versatile and adaptable framework that exploits Computer Vision and the Robot Operating System (ROS) to facilitate pick-and-place operations within robotic cells, offering a comprehensive solution for handling and sorting random-flow objects on conveyor belts. Designed to be easily configured and reconfigured, it accommodates ROS-compatible robotic arms and 3D vision systems, ensuring adaptability to different technological requirements and reducing deployment costs. Experimental results demonstrate the framework’s high precision and accuracy in manipulating and sorting tested objects. Thus, this framework enhances the efficiency and flexibility of industrial robotic systems, making object manipulation more adaptable for unpredictable manufacturing environments.

Pick and Place Control of a 3-DOF Robot Manipulator Based on Image and Pattern Recognition

Board games like chess serve as an excellent testbed for human–robot interactions, where advancements can lead to broader human–robot cooperation systems. This paper presents a chess-playing robotic system to demonstrate controlled pick and place operations using a 3-DoF manipulator with image and speech recognition. The system identifies chessboard square coordinates through image processing and centroid detection before mapping them onto the physical board. User voice input is processed and transcribed into a string from which the system extracts the current and destination locations of a chess piece with a word error rate of 8.64%. Using an inverse-kinematics algorithm, the system calculates the joint angles needed to position the end effector at the desired coordinates actuating the robot. The developed system was evaluated experimentally on the 3-DoF manipulator with a voice command used to direct the robot movement in grasping a chess piece. Consideration was made involving both the own pieces as well as capturing the opponent’s pieces and moving the captured piece outside the board workspace.

A robust assessment method of point cloud quality for enhancing 3D robotic scanning

Point clouds are widely used to construct models of workpieces using 3D scanners, especially where high-quality robotic and automatic 3D scanning is required in industries and manufacturing. In recent years, Point Cloud Quality Assessment (PCQA) has garnered increasing attention as it provides quality scores for entire point clouds, addressing issues such as downsampling and compression distortions. However, current PCQA methods cannot provide specific and local quality scores, which are necessary to facilitate rescanning and recompletion in 3D robotic scanning. Additionally, 3D data produced by robotic view-planning algorithms are usually considered the final result, where PCQA is typically not involved. In this paper, we bridge the gap between PCQA methods and practical robotic 3D scanning. We propose a no-reference PCQA method that recognizes sparse regions during 3D scanning, providing both local and overall quality scores. Unlike traditional methods that primarily consider density as a key metric, our method assumes that an expected 3D scan will have a uniformly distributed point cloud on surfaces. We analyze the quality of points by using geometric information from surfaces fitted to these points, which are mapped to a 2D distribution based on specified distances and angles. We conducted experiments on various datasets, including both synthetic and public datasets, to evaluate the accuracy and robustness of our method. The results show that our method can represent the quality on surfaces more accurately and robustly than density calculation methods. Additionally, it outperforms most existing PCQA methods in scenarios of downsampling, which is a common challenge in high-quality 3D scanning applications. The performance of our quality enhancement experiments on practical 3D scanning, conducted towards the end of our study, demonstrates significant potential for real-world applications. The related code is released at https://github.com/leihui6/PQA.