3D Robot Research Articles

Advancements in robotic arm-based 3D bioprinting for biomedical applications

Abstract 3D bioprinting emerges as a critical tool in biofabricating functional 3D tissue or organ equivalents for regenerative medicine. Bioprinting techniques have been making strides in integrating automation, customization, and digitalization in coping with diverse tissue engineering scenarios. The convergence of robotic arm-based 3D bioprinting techniques, especially in situ 3D bioprinting, is a versatile toolbox in the industrial field, promising for biomedical application and clinical research. In this review, we first introduce conceptualized modalities of robotic arm-based bioprinting from a mechanical perspective, which involves configurative categories of current robot arms regarding conventional bioprinting strategies. Recent advances in robotic arm-based bioprinting in tissue engineering have been summarized in distinct tissues and organs. Ultimately, we systematically discuss relative advantages, disadvantages, challenges, and future perspectives from bench to bedside for biomedical application.

An inverse kinematic method for non-spherical wrist 6DOF robot based on reconfigured objective function

An inverse kinematic method for non-spherical wrist 6DOF robot based on reconfigured objective function

Optimal Fuzzy-FOPID, Fuzzy-PID Control Schemes for Trajectory Tracking of 3DOF Robot Manipulator

The present study explores the guidance of a robotic arm along a predefined path by implementing an optimal fuzzy fractional order PID controller-based control strategy. This method serves as a means to address the nonlinearity and unpredictability of the robotic manipulator, contingent upon the fuzzy logic controller's specifications and the employment of a clonal selection algorithm. The dynamic equation of the manipulator was considered as an initial point, followed by designing a fuzzy controller for this purpose. To validate the effectiveness of this approach, it was compared to other techniques, such as Fuzzy, Fuzzy-PID, and fuzzy-FOPID controllers, with PID and FOPID controller parameters optimized using clonal selection algorithms. Simulation results reveal that the fuzzy-FOPID variant outperformed other methods under varying load conditions and model uncertainties, using SIMULINK/MATLAB 2014a.

Robotically 3D printed architectural membranes from ambient dried cellulose nanofibril-alginate hydrogel

Cellulose nanofibril hydrogel mixed with an aqueous solution of sodium alginate is a novel bio-based material suitable for 3D printing of lightweight membranes with exquisite properties and sustainable traits. However, fundamental knowledge enabling its applications in architectural design is still missing. Hence, this study examines the macro-scale features of lightweight membranes from cellulose nanofibril-alginate hydrogel, relevant for the design of various interior architectural products, such as wall claddings, ceiling tiles, room partitions, tapestries, and window screens. Through iterative prototyping experiments involving robotic 3D printing of lightweight membranes, their upscaling potential is demonstrated. Correlations between toolpath designs and shrinkages are also characterized, alongside an in-depth analysis of coloration changes upon ambient drying. Further, the tunability potential of various architectural features, enabled by bespoke 3D printing toolpath design, is discussed and exemplified. The aim is to expose the wide palette of design possibilities for cellulose nanofibril-alginate membranes, encompassing variations in curvature, porosity, translucency, texture, patterning, pliability, and feature sizes. The results comprise an important knowledge foundation for the design and manufacturing of custom lightweight architectural products from cellulose nanofibril-alginate hydrogel. These products could be applied in a variety of new bio-based, sustainable interior building systems, replacing environmentally harmful, fossil-based solutions.

Geometric approach to solving inverse kinematics of six DOF robot with spherical joints

Inverse kinematics is a fundamental concept in robotics that plays a crucial role in a robot’s ability to perform tasks. In this contribution, we propose a novel geometric approach based on vector calculus to solve the inverse kinematics problem. The primary advantage of this approach originates from the solutions, which exhibit a linear form and uncoupled equations. To validate the effectiveness and correctness of our proposed method, we constructed a six-degrees-of-freedom robot. This robot is controlled by an Arduino Mega 2650 on which we have implemented the inverse kinematics algorithm. The validation process involved considering various desired trajectories of the end-effector, which were simulated in Matlab and then performed by the physical robot. Importantly, our findings confirm that the end-effector successfully tracks the predefined trajectories. Furthermore, we conducted a comparative analysis between Paul’s method and the results obtained from joint angles using our proposed approach. Interestingly, our study reveals a significant similarity between the two sets of results, reaffirming the accuracy and validity of the approach presented in this study.

Vacuum-powered soft actuator with oblique air chambers for easy detachment of artificial dry adhesive by coupled contraction and twisting

ABSTRACT A gecko foot-inspired, mushroom-shaped artificial dry adhesive exploiting intermolecular forces between microstructure and surface has drawn research attention for its strong adhesive force. However, the high pull-off strength corresponding to the adhesive force matters when detaching fragile substrates. In this study, we report a vacuum-powered soft actuator having oblique air chambers and a dry adhesive. The soft actuator performs coupled contraction and twisting by applying negative pneumatic pressure inward and exhibits not only high pull-off strength but also easy detachment. This effective detachment can be achieved thanks to the twisting motion of the soft actuator. The detachment performances of the actuator models are assessed using a 6-degrees-of-freedom robot arm. Results show that the soft actuators exhibit remarkable pull-off strength decrement from ~20 N cm−2 to ~2 N cm−2 due to the twisting. Finally, to verify a feasible application of this study, we utilize the inherent compliance of the actuators and introduce a glass transfer system for which a glass substrate on a slope is gripped by the flexibility of the soft actuators and delivered to the destination without any fracture.

A PID-Controlled Approach in the Design of a Physiotherapy Robot for Upper Arm Rehabilitation

The main objective to develop controller for the rebab robot, therefore to achieve the main objective, the subsequent objective is to formulate the mathematical modelling of 2 DOF of upper arm, to develop upper arm rebab robot, evaluate the performance of the controller and facilitate the creation of new and more improved devices for physiotherapy robot ; therefore, this research will investigate rehabilitation robots, it have shown a high potential for improving the patient’s mobility, improving their functional movements and assisting in daily activities. However, this technology is still an emerging field and suffers from several challenges like compliance control and dynamic uncertain caused by the human–robot collaboration. The main challenge addressed in this thesis is to develop a controller to the rehab robot and formulate the mathematical modelling of 2 DOF of upper arm. Ensure that the exoskeleton robot provides a suitable compliance control that allows it to cooperate perfectly with humans even if the dynamic model of the exoskeleton robot is uncertain. The PID controller is a widely used control algorithm that aims to regulate a system's output by continuously adjusting its input. In the context of a two-degree-of-freedom (2-DOF) robot arm, the PID controller plays a crucial role in achieving precise and accurate arm movements. The Proportional-Integral-Derivative (PID) controller employs three main components to achieve control: proportional, integral, and derivative terms.

Robotik Kodlama ve 3D Yazıcı Uygulamalarında STEM Temelli İnovatif Düşünme Becerileri ve Dijital Teknolojiye Yönelik Tutumları

The aim of this study is to determine STEM-based innovative thinking skills and attitudes towards digital technology in robotic coding and 3D printer applications with 45 teacher candidates participating in the research. Quantitative research method was used. The research lasted 12 weeks. Innovative thinking skills scale and attitude scales towards digital technology were used. The research was conducted in the form of pretest and posttest. It is seen that there is a significant positive difference in the innovative thinking skills of the teacher candidates and their attitudes towards digital technology. In the STEM-based research, robotic coding and 3D printing training was given. In this context, three different applications were made regarding traffic lights. In robotic coding applications, Tinkercad, Arduino IDE and Fritzing programs for circuit diagrams, Tinkercad 3D in 3D printing applications and Zaxe PLA for slicing were taught. (SPSS-21.00) program was used to analyze the data and t test was applied for dependent samples. It was determined that the teacher candidates had innovative thinking skills attitudes (t=-23.33; p

LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments

Accurate localization of robots in unstructured environments poses challenges due to low localization accuracy and local trajectory oscillation caused by complex feature points when using Euclidean-based filtering methods. In this study, we propose a novel 3D robot localization method named LIF-M that leverages a manifold-based approach in conjunction with an unscented Kalman filter (UKF-M). Additionally, a relocalization algorithm is designed to ensure localization stability. The proposed method addresses the limitations of conventional Euclidean-based filtering methods by incorporating manifold-based techniques, providing a more comprehensive representation of the complex geometric features. We introduce the manifold concept, where the relevant definition is defined and utilized within the LIF-M framework. By combining left and right invariants, we effectively reduce noise uncertainty, resulting in improved localization accuracy. Moreover, we employ sigma points as a matrix representation of the state points’ space in order to seamlessly transition between the matrix space and the vector representation of the tangent space. Experimental tests and error calculations were conducted to evaluate the performance of various algorithm frameworks, and the results demonstrated the importance of the manifold-based approach for accurate attitude estimation. Compared to the standard UKF, the manifold space equips LIF-M with better robustness and stability in unstructured environments.

Kinematic model calibration of a collaborative redundant robot using a closed kinematic chain

In this paper, we propose a novel approach for the kinematic calibration of collaborative redundat robots, focusing on improving their precision using a cost-effective and efficient method. We exploit the redundancy of the closed-loop kinematic chain by utilizing a spherical joint, enabling precise definition of the robot end-effector position while maintaining free joint motion in the null space. Leveraging the availability of joint torque sensors in most collaborative robots, we employ a kinesthetic approach to obtain constrained joint motion for calibration. An optimization approach is utilized to determine the optimal kinematic parameters based on measured joint positions and a constrained end-effector position defined by the spherical joint. The effectiveness of the proposed method is demonstrated and validated on the Franka Emika Panda robot, a 7-DoF robot. Results indicate a significant enhancement in absolute accuracy, with comparable performance to more expensive sensor systems such as optical measurement systems. Our approach offers a practical and cost-effective solution for improving the precision of collaborative robots.

Six DOF robot: inverse kinematics solution to path planning for intersecting pipes for welding operation and inverse Jacobian comparison

Six DOF robot: inverse kinematics solution to path planning for intersecting pipes for welding operation and inverse Jacobian comparison

Modeling and Simulation of Robotic Palpation to Detect Subsurface Soft Tissue Anomaly for Presurgical Assessment

Abstract Surgical Haptics is an emergent field of research to integrate and advance the sense of robotic touch in laparoscopic tools in robot-assisted minimally invasive surgery. Haptic feedback from the tooltip and soft tissue surface interaction during robotic palpation can be leveraged to detect the texture and contour of subsurface geometry. However, precise force modulation of the robotic palpating probe is necessary to determine stiff inclusions of the anatomy and maneuver successive manipulation tasks during surgery. This paper focuses on investigating the layered deformations associated with different force profiles involved in manipulating the superficial anatomy of soft tissues during dynamic robotic palpation to determine the underlying anomaly. A realistic three-dimensional (3D) cross-sectional soft tissue phantom with anatomical layers and tumor, as an anomaly, is designed, modeled, and analyzed to examine the effects of oriented palpating forces (0–5 N) of a 7 DOF robot arm equipped with a contoured palpation probe. Finite element static structural analysis of oriented robotic palpation on the developed 3D soft tissue phantoms (with and without anomaly) reveals the soft tissue layer deformations and associated strains needed to identify presence of stiffer inclusions or anomaly during Robotic palpation. The finite element analysis study shows that the difference in deformations of soft tissue layers (e.g., underlying myofascial layers) under stiffer inclusions at different force levels can facilitate haptic feedback to acquire information about subsurface tumors. The deformation variations are further compared to assess better palpation orientations for subsurface anomaly detection.

Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot

Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot

Point Cloud Completion Via Skeleton-Detail Transformer.

Point cloud shape completion plays a central role in diverse 3D vision and robotics applications. Early methods used to generate global shapes without local detail refinement. Current methods tend to leverage local features to preserve the observed geometric details. However, they usually adopt the convolutional architecture over the incomplete point cloud to extract local features to restore the diverse information of both latent shape skeleton and geometric details, where long-distance correlation among the skeleton and details is ignored. In this work, we present a coarse-to-fine completion framework, which makes full use of both neighboring and long-distance region cues for point cloud completion. Our network leverages a Skeleton-Detail Transformer, which contains cross-attention and self-attention layers, to fully explore the correlation from local patterns to global shape and utilize it to enhance the overall skeleton. Also, we propose a selective attention mechanism to save memory usage in the attention process without significantly affecting performance. We conduct extensive experiments on the ShapeNet dataset and real-scanned datasets. Qualitative and quantitative evaluations demonstrate that our proposed network outperforms current state-of-the-art methods.

Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

Three dimensional (3D) robot measurement systems, with binocular planar structured light cameras (3D cameras) installed at the terminal flange of robots, are widely used to measure complete workpieces by stitching point clouds obtained from various sampled poses. To ensure accurate stitching, hand–eye calibration is necessary. However, 3D cameras often demonstrate low precision in measuring jumping edges and vertices, resulting in challenges when selecting appropriate calibrators and calibration methods for hand–eye calibration. We propose a simple and high‐precision hand–eye calibration method for 3D robot measurement systems. Initially, a single sphere is utilized as the calibrator, and its poses are observed using specific robotic motions. Subsequently, the hand–eye calibration problem is formulated as a well‐known equation, , relying solely on the high‐precision relative pose of the robot. Finally, a novel simultaneous solution for and the corresponding closed‐form initial solution are introduced. Simulations and experiments confirm that the proposed method exhibits high noise resistance and achieves high‐precision calibration, even with a limited calibration data quantity. Compared with traditional methods, the fitting error of the proposed method can be reduced from over 0.9 mm to less than 0.6 mm.

Nonlinear Functional Observer Design for Robot Manipulators

In this paper, a nonlinear functional observer (NFO) is first proposed for the control design of robot manipulators under model uncertainties, external disturbances, and a lack of joint velocity information. In principle, the proposed NFO can estimate not only lumped disturbances and uncertainties but also unmeasurable joint velocities, which are then fed back into the main controller. Compared to the well-known ESO design, the proposed NFO has a simpler structure, more accurate estimations, and less computational effort, and consequently, it is easier for practical implementation. Moreover, unnecessary observations of joint displacements are avoided when compared to the well-known extended state observer (ESO). Based on the Lyapunov theory, globally uniformly ultimately bounded estimation performance is guaranteed by the proposed NFO. Consequently, it is theoretically proven that the estimation performances of the NFO are better than those of the ESO. Simulations with a two-degree-of-freedom (2-DOF) robot manipulator are conducted to verify the effectiveness of the proposed algorithm in terms of not only the estimation performance but also the closed-loop control performance.

A robotic 3D printer for UV-curable thermosets: dimensionality prediction using a data-driven approach

ABSTRACT This paper presents a robotic 3D printer specifically designed for ultraviolet (UV)-curable thermosets, whose printing parameters can be selected by using a predictive modeling strategy. A specialized extruder head was designed and integrated with a UR5e robotic arm. Software packages were developed to enable the communication between the extruder head and the robotic arm, and control systems were implemented to regulate the printing process. A predictive approach using either a feedforward neural network (FNN) or convolution neural network (CNN) is proposed for estimating the dimensions of future prints based on the process parameters. This enables selection of the appropriate parameters for high-quality prints. This strategy aims to decrease expensive trial-and-error campaigns for material and printing parameter tuning. Experimental results demonstrate the capabilities of the robotic 3D printer and the accuracy of the predictive approach.

A backdrivable 6-dof parallel robot for sensorless dynamically interactive tasks

In order to combine low mechanical impedance and sufficient interaction forces, a six-degree-of-freedom robot featuring a mechanically backdrivable transmission with a low reduction ratio is proposed in this paper. Also, a parallel architecture is used in order to allow the use of relatively large and high-torque actuators. The architecture of the robot is first presented. Then, the geometry of the robot and the ratio of the transmissions are analysed in order to ensure that the selected design parameters yield the desired properties. Shock absorbers are also included in the legs of the robot so that impacts can be applied, e.g., for the assembly of some components. Then, algorithms are presented for Cartesian stiffness control and gravity compensation of the robot. Finally, a prototype is presented and validated experimentally for position control, impedance control and impact generation. The parallel robot presented in this paper is of particular interest for industrial assembly tasks.

An Analytical Method for Sensitivity Analysis of Rigid Multibody System Dynamics Using Projective Geometric Algebra

Abstract The analytical sensitivity analysis, i.e., the analytical first-order partial derivatives of dynamical equations, is one key to improving descent-based optimization methods for motion planning and control of robots. This paper proposes an efficient algorithm that recursively evaluates the analytic gradient of the dynamical equations of a multibody system. The theory of projective geometric algebra (PGA) is used to generate the algorithm. It provides a systemic and geometrically intuitive interpretation for the multibody system dynamics, and the resulting algorithm is highly efficient, with concise formula. The algorithm is first applied to the open-chain system and extended for the cases when kinematic loops are contained. The runtime varying with respect to the degree-of-freedom (DOF) of the system is analyzed. The results are compared with that obtained from the algorithm based on spatial vector algebra (SVA) using open-source matlab codes. A 2DOF serial robot, a 3DOF robot with a kinematic loop and the PUMA560 robot are used for the validation of the minimum-effort motion planning, and it is verified that the proposed algorithm improves the efficiency.

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain's Cognitive Mechanism.

Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat's brain. Based on these, this paper proposes a navigation path search and optimization method for mobile robots based on the rat brain's cognitive mechanism. The aim is to enhance the navigation efficiency of mobile robots. The mechanism of this method is based on developing a navigation habit. Firstly, the robot explores the environment to search for the navigation goal. Then, with the assistance of boundary vector cells, the greedy strategy is used to guide the robot in generating a locally optimal path. Once the navigation path is generated, a dynamic self-organizing model based on the hippocampal CA1 place cells is constructed to further optimize the navigation path. To validate the effectiveness of the method, this paper designs several 2D simulation experiments and 3D robot simulation experiments, and compares the proposed method with various algorithms. The experimental results demonstrate that the proposed method not only surpasses other algorithms in terms of path planning efficiency but also yields the shortest navigation path. Moreover, the method exhibits good adaptability to dynamic navigation tasks.