Manipulator System Research Articles

Multiplexed microfluidic chip based flow injection analysis system for simultaneous determination of Fe(III), Mn(II) and Cu(II)

The development of microfluidic flow injection analysis (FIA) system for simultaneous and real-time detection of multiple metal ions is of great importance for environmental monitoring, food safety and industrial field. In this work, a multiplexed microfluidic chip based FIA system was fabricated for simultaneous determination of three metal ions, by using Fe(III), Mn(II) and Cu(II) as models. The multiplexed microfluidic chip based FIA system is composed of three parts, including a multi-channel FIA system for solution manipulation, a group of microfluidic chips for reagents mixing and reaction, and one fiber optic spectrometer for fast and real-time absorbance signal detection. The sample solution and chromogenic reagents were injected into the microfluidic chips to carry out specific colorimetric reactions. Fe(III), Mn(II) and Cu(II) reacts with 5-sulfosalicylic acid, formaldoxime, and bis(cyclohexanone) oxalyldihydrazone, respectively, to generate orange, ember and blue colored solution with peak absorbance at 420 nm, 450 nm, and 600 nm, respectively. The absorbance signal of the generated three channels of solution can be sequentially detected by using only one mini spectrometer. The linear detection range for Fe(III), Mn(II) and Cu(II) is 0.1–20 mg/L, 0.1–12 mg/L and 0.2–12 mg/L, respectively. The limit of detection for Fe(III), Mn(II) and Cu(II) is 0.07, 0.02 and 0.1 mg/L respectively. And the limit of quantification for Fe(III), Mn(II) and Cu(II) is 0.2, 0.07 and 0.3 mg/L, respectively. The recoveries of spiked Fe (III), Mn (II), and Cu (II) in real water samples are between 95.3 % to 106 %. Finally, the developed microfluidic chip based FIA system was successfully applied to simultaneous determination of three metal ions in environmental water samples and zinc smelting solution from zinc hydrometallurgy industry with satisfied results.

Adaptive practical prescribed-time control for uncertain nonlinear systems with time-varying parameters

In this paper, adaptive practical prescribed-time (PPT) control is proposed for a class of uncertain nonlinear systems with time-varying parameters and unmodeled dynamics. By constructing a novel time-varying scaling function and utilizing nonlinear mapping, the PPT control is successfully resolved. The dynamical uncertainties resulting from unmodeled dynamics are estimated by employing an auxiliary available signal, and the unknown continuous terms are handled by the aid of radial basis function neural networks (RBFNNs). A novel adaptive control method is developed by introducing the compensating signals and dynamic surface control as well as practical prescribed-time control. All the signals involved are proved to be semi-globally uniformly ultimately bounded, and the tracking error could enter the pre-specified convergence region within a pre-specified time. The robotic manipulator system is used to demonstrate the effectiveness of the proposed control approach.

Design and experiment of facility elevated planting strawberry continuous picking manipulator

Strawberry picking manipulator is one of the key factors affecting the effectiveness of robot picking operations, elevated strawberries cultivated off the ground with hanging fruits provide convenient conditions for automatic strawberry picking. In order to adapt to the small space of elevated strawberry picking operation and to solve the problem of discontinuous picking process, a P-P-R-P type continuous strawberry picking manipulator was proposed. Based on the elevated cultivation mode and the clamping-shear picking method, the structural parameters and control system of the continuous picking manipulator were determined. The kinematics model of the continuous picking manipulator was established, the Jacobian condition number and performance of each joint in the picking operation were solved, and its flexibility and operability were analyzed. According to the growth characteristics of elevated strawberries, the picking simulation routes and operation cycles were set. Using MATLAB software, the simulation tests were carried out to analyze the working space of the continuous picking manipulator, the maximum speed and acceleration changes of each joint. In order to improve the success rate of strawberry picking, the operating parameters such as rotational speed, end picking angle and clamping position of the continuous picking manipulator were optimized using Box-Behnken test. Based on the simulation results and parameter optimization results, a continuous picking manipulator and motion control system were constructed and validation tests were conducted. The results showed that the average picking success rate of the continuous picking manipulator was 74.21 %, and the average picking efficiency was 12.87 s per piece. In conclusion, the P-P-R-P type continuous picking manipulator proposed in this study meets the automated operation requirements of strawberry picking and can be adapted to the elevated operating environment.

Adaptive neural boundary control for multi-agent manipulators system with uncertainties through cooperative disturbance observers network

This paper addresses vibration control problem of multi-agent flexible manipulators systems in the presence of simultaneous uncertainty and unknown external disturbance. Particularly, the goal is to suppress vibration of both flexible link and joint angular. In this paper, the dynamic model of the considered flexible manipulator is described by the fourth order partial differential equation. Without control, the system is unstable and vibrate constantly due to initial states, the external unknown disturbances and system uncertainties. To compensate the uncertainty in each agent, the neural networks are employed and novel adaptation laws are developed to update weighting parameters in the neural networks. While for the compensation of the external disturbance a cooperative network of disturbance observers is proposed to enhance the observation reliability. With the resulting estimations of uncertainties and the unknown disturbance, adaptive distributed boundary controllers are derived to suppress vibration in-domain and keep joint angular position to zero. The closed-loop system is proven to be uniform ultimately bounded through Lyapunov stability theory. Numerical simulations result shows that compared with the proportional–derivative control, the proposed method almost reduces all overshoot and steady-state error.

Achieving Significant Multilevel Modulation in Superior-quality Organic Spin Valve.

Organic semiconductors, characterized by their exceptionally long spin relaxation times (≈ms) and unique spinterface effects, are considered game-changers in spintronics. However, achieving high-performance and wide-range tunable magnetoresistance (MR) in organic spintronic devices remains challenging, severely limiting the development of organic spintronics. This work combines straintronic multiferroic heterostructures with organic spin valve (OSV) to develop a three-terminal OSV device with a gate structure. The device exhibits a record-high MR ratio of 281% which 10 times higher than the average in polymer systems. More importantly, this work can perform multilevel writing operations on the device using gate voltages and create at least 10 stable spin-dependent working states within a single device. Both experiments and theoretical calculations confirm such an extraordinary tunability range originates from the synergistic effects of strain and charge accumulation that amplified by the spinterface. This study demonstrates the potential of OSV systems for efficient spin manipulation and highlights the spinterface as an ideal platform for amplifying spin effects for next-generation spintronic devices.

Neural network prescribed-time observer-based output-feedback control for uncertain pure-feedback nonlinear systems

To prevent the complicated backstepping design, as well as the coupled design of state observer/disturbance approximator and baseline controller that makes the parameter synthesis difficult, this article proposes a neural network (NN) prescribed-time observer-based output-feedback control approach for uncertain pure-feedback nonlinear systems. After reformulating the original system into a canonical form with matched lumped disturbance, an adaptive NN prescribed-time observer (NNPTO) is developed to quickly provide accurate estimates of unknown states and disturbance. Then the observer is integrated with designed non-backstepping state-feedback controller to construct an output-feedback control scheme. Three features distinguish our approach from existing non-backstepping methods: (1) the accurate estimation is realized in a finite time prescribed through a single parameter; (2) the design of the state observer/disturbance approximator and baseline controller is decoupled, which eases the synthesis process and makes lots of existing non-backstepping state-feedback controllers compatible with our output-feedback control approach; (3) The NNPTO has trivial oscillation and small observation peaking errors under large disturbance, which effectively improves the output tracking performance. Numerical examples and an application example of a rigid single-link manipulator system are presented to demonstrate the effectiveness and practicability of our approach.

Event‐Triggered Robust Control of Robot Manipulators

ABSTRACTThis paper proposes a new event‐triggered robust control system for robot manipulators, based on two event‐triggered mechanisms (ETMs), which lead to intermittent communication not only for the sensor‐to‐controller channel but also for the controller‐to‐actuator channel. The proposed control system enjoys the advantage of requiring fewer resources of data communication and less signal updating of the control actuators. The control performance of the proposed control system is analyzed based on the Lyapunov approach. Furthermore, the absence of the Zeno behavior in the triggering sequence is proved rigorously. Finally, experiments are carried out on a two‐link robot manipulator system to support the theoretical results.

Development of ptxD/Phi as a new dominant selection system for genetic manipulation in Cryptococcus neoformans.

Cryptococcus neoformans is a globally distributed pathogenic fungus posing a significant threat to immunocompromised individuals, particularly those with HIV/AIDS. Effective genetic manipulation tools are essential for understanding its biology and developing new therapies. However, current genetic tools, including the variation of versatile selectable markers, are limited. This study develops and validates the phosphite dehydrogenase gene (ptxD)/phosphite (Phi) selection system as a non-antibiotic selectable marker for genetic manipulation in C. neoformans. A codon-optimized ptxD gene from Pseudomonas stutzeri was cloned under the TEF promoter. Using the transient CRISPR-Cas9 coupled with electroporation system, we integrated the ptxD gene into the C. neoformans genome and assessed the impact of ptxD integration on cell growth and virulence factors. The ptxD/Phi system effectively selected transformed cells on Phi-containing media. Growth assays showed that ptxD integration did not adversely affect cell growth or key virulence factors, including pleomorphism, capsule size, and melanin production. Additionally, we successfully disrupted the ADE2 gene using this system, confirming its applicability for gene deletion. Taken together, the ptxD/Phi system provides a robust and versatile tool for genetic manipulation in C. neoformans, facilitating further research into its biology and pathogenicity.IMPORTANCECryptococcus neoformans is a type of fungus that can cause serious illnesses in people who have weakened immune systems, like those with HIV/AIDS. To better study this fungus and find new treatments, scientists need tools to change its genes in precise ways. However, the current tools available for this are somewhat limited. This research introduces a new tool called the phosphite dehydrogenase gene/phosphite system, which does not rely on antibiotics to work. It uses a gene from a different bacterium that helps select and grow only the fungus cells that have successfully incorporated new genetic information. This is particularly useful because it does not interfere with the normal growth of the fungus or the features that make it harmful (like its ability to change shape or produce protective coatings). By making it easier and more effective to manipulate the genetics of C. neoformans, this tool opens up new possibilities for understanding how this fungus operates and for developing therapies to combat its infections. This is crucial for improving the treatment of infections in vulnerable populations.

Sensorless contact force estimation and robust impedance control for a quadrotor manipulation system

The research on aerial manipulation systems has been increased rapidly in recent years. These systems are very attractive for a wide range of applications due to their unique features. However, dynamics, control and manipulation tasks of such systems are quite challenging because they are naturally unstable, have very fast dynamics, have strong nonlinearities, are very susceptible to parameters variations due to carrying a payload besides the external disturbances, and have complex inverse kinematics. In addition, the manipulation tasks require estimating (applying) a certain force of (at) the end-effector as well as the accurate positioning of it. Thus, in this article, a robust force estimation and impedance control scheme is proposed to address these issues. The robustness is achieved based on the Disturbance Observer (DOb) technique. Then, a tracking and performance low computational linear controller is used. For teleoperation purpose, the contact force needs to be identified. However, the current developed techniques for force estimation have limitations because they are based on ignoring some dynamics and/or requiring of an indicator of the environment contact. Unlike these techniques, we propose a technique based on linearization capabilities of DOb and a Fast Tracking Recursive Least Squares (FTRLS) algorithm. The complex inverse kinematics problem of such a system is solved by a Jacobin based algorithm. The stability analysis of the proposed scheme is presented. The algorithm is tested to achieve tracking of task space reference trajectories besides the impedance control. The efficiency of the proposed technique is enlightened via numerical simulation.

Quasi-static modeling of a cable-driven continuum manipulator considering non-smooth cable-hole friction and experimental verification

In cable-driven continuum manipulator (CDCM) systems, the driving hysteresis effect due to non-smooth friction significantly affects motion control. In this paper, a quasi-static model of a CDCM considering non-smooth friction is proposed. The finite element method is used to discretize the backbone, neglecting the inertial effect in the driving process, and the nodal force vector for the backbone is obtained. Then, by considering the attenuation and non-smoothness of friction, the nodal force vector for the driving cables is obtained, and the tangent stiffness matrix of the nodal force vector is derived in detail. Subsequently, cable reeling/unreeling experiments were also conducted, and the hysteresis loops obtained were consistent with the theoretical model, thus verifying the validity of the non-smooth model. Finally, trajectory tracking simulations along spatial curves are conducted, and the influence of sliding friction and loading history on the evolution of driving force is studied.

Process-Threshold-Based Fuzzy Adaptive Prescribed Performance Event-Triggered Tracking Control for a Manipulator System

Process-Threshold-Based Fuzzy Adaptive Prescribed Performance Event-Triggered Tracking Control for a Manipulator System

Rotaxanes with a photoresponsive macrocycle modulate the lipid bilayers of large and giant unilamellar vesicles

Rotaxanes equipped with actuators hold great potential for developing highly functional molecular machines. Such systems could significantly enhance our ability to study and manipulate biological and artificial membranes. Here, we introduce a rotaxane with a ring featuring two azobenzene photoswitches, which retain their photoreversibility and can be stochastically shuttled along the axle in solution. Studies in model bilayers, supported by molecular dynamics simulations, show how azobenzene photoswitching alters the interaction of rotaxanes with surrounding lipids, leading to changes in lipid packing. Such changes in the lipid bilayer were leveraged to induce the light-triggered release of sulforhodamine B from large unilamellar vesicles. Additionally, light activation of the rotaxanes is shown to induce reversible contraction and expansion of giant unilamellar vesicles. The results provide novel insights into the interactions and operation of rotaxanes in lipid bilayers and their impact on membrane properties. This will aid in developing systems for precise membrane manipulation for applications in biomedicine and bioengineering.

MICROCONTROLLER MANAGEMENT SYSTEM OF MANIPULATOR MANAGE

Currently, most manipulators are driven by a DC or AC motor. The encoder is used as a speed positioning and feedback device in the manipulator control system.The spectrum of available solutions in the field of microcontroller control systems of manipulators is considered in the article. The circuit of the controller board has been developed to control the transfer of the manipulator manga.The analysis of the element base included in the composition of the control board has been performed. The manipulators used for the automation of biochemical analysis allow to improve the quality of the conducted analysis and the repeatability of the robot's work. Also, due to continuous performance of the work, it creates conditions to increase productivity as a whole. Keywords: manipulation, robot, microcontroller, manipulator, transmission.

A Comparative Analysis Among Dynamics Modeling Approaches for Space Manipulator Systems

Abstract This paper presents a comparative analysis on the space manipulator systems dynamics modeling approaches, namely, the standard approach (SA) and the dual quaternion based dynamics modeling approach. A detailed analysis supported by the results from numerical simulations, comparing the two approaches in terms of operational count and execution time, has been presented to determine which approach is computationally and temporally efficient.

Capturing forceful interaction with deformable objects using a deep learning-powered stretchable tactile array

Capturing forceful interaction with deformable objects during manipulation benefits applications like virtual reality, telemedicine, and robotics. Replicating full hand-object states with complete geometry is challenging because of the occluded object deformations. Here, we report a visual-tactile recording and tracking system for manipulation featuring a stretchable tactile glove with 1152 force-sensing channels and a visual-tactile joint learning framework to estimate dynamic hand-object states during manipulation. To overcome the strain interference caused by contact with deformable objects, an active suppression method based on symmetric response detection and adaptive calibration is proposed and achieves 97.6% accuracy in force measurement, contributing to an improvement of 45.3%. The learning framework processes the visual-tactile sequence and reconstructs hand-object states. We experiment on 24 objects from 6 categories including both deformable and rigid ones with an average reconstruction error of 1.8 cm for all sequences, demonstrating a universal ability to replicate human knowledge in manipulating objects with varying degrees of deformability.

Impulsive tracking synchronization control of networked robotic manipulator systems in the task space

This study investigates the impulsive tracking synchronization control of networked robotic manipulator systems based on Lagrangian systems. In the task space, the end-effector positions of networked robotic manipulators can synchronize and track a desired trajectory by designing an impulsive controller. Based on impulsive control, some algebraic synchronization criteria are derived. As a direct application of the theoretical results, tracking synchronization of six double-link robotic manipulators in the task space is discussed in detail. Numerical simulations are given to demonstrate the effectiveness and feasibility of the proposed control strategy.

Design and realization of an isomorphic master-slave robotic system for precise sub-micrometer manipulations in ophthalmic surgery

Design and realization of an isomorphic master-slave robotic system for precise sub-micrometer manipulations in ophthalmic surgery

Distributed Neural Adaptive Impedance Control for Cooperative Manipulation With Unknown Objects.

Existing cooperative manipulation methods for multiple manipulator systems usually assume that the grasp matrix and the desired trajectory of each manipulator are known in advance. In this work, distributed neural adaptive impedance control (AIC) strategies integrating fully distributed observers are proposed to remove both limitations. Specifically, two fully distributed finite-time observers are designed to estimate the actual and ideal states of the reference point without using global information. The estimates of the grasp matrix and the desired trajectory of each end-effector (EE) are then obtained by kinematic constraints and the estimates of the reference point's states. At the controller development, a distributed adaptive impedance model is established to achieve an adaptive trade-off between tracking performance and compliance. Then, distributed neural network (NN)-based tracking control strategies are developed to asymptotically realize the desired adaptive impedance dynamics in the presence of uncertainties. Additionally, a virtual energy tank (EK) is employed to interact with the impedance system to correct the adaptive impedance laws for system passivity. A simulation for four mobile manipulators tightly cooperative transport an unknown object is carried out to demonstrate the established results.

Robotic manipulation of cardiomyocytes to identify gap junction modifiers for arrhythmogenic cardiomyopathy.

Arrhythmogenic cardiomyopathy (ACM) is a leading cause of sudden cardiac death among young adults. Aberrant gap junction remodeling has been linked to disease-causative mutations in plakophilin-2 (PKP2). Although gap junctions are a key therapeutic target, measurement of gap junction function in preclinical disease models is technically challenging. To quantify gap junction function with high precision and high consistency, we developed a robotic cell manipulation system with visual feedback from digital holographic microscopy for three-dimensional and label-free imaging of human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). The robotic system can accurately determine the dynamic height changes in the cells' contraction and resting phases, microinject drug-treated healthy and diseased iPSC-CMs in their resting phase with constant injection depth across all cells, and deposit a membrane-impermeable dye that solely diffuses between cells through gap junctions for measuring the gap junction diffusion function. The robotic system was applied toward a targeted drug screen to identify gap junction modulators and potential therapeutics for ACM. Five compounds were found to dose-dependently enhance gap junction permeability in cardiomyocytes with PKP2 knockdown. In addition, PCO 400 (pinacidil) reduced beating irregularity in a mouse model of ACM expressing mutant PKP2 (R735X). These results highlight the utility of the robotic cell manipulation system to efficiently assess gap junction function in a relevant preclinical disease model, thus providing a technique to advance drug discovery for ACM and other gap junction-mediated diseases.

Visual Servoing for Aerial Vegetation Sampling Systems

This research describes a vision-based control strategy that employs deep learning for an aerial manipulation system developed for vegetation sampling in remote, dangerous environments. Vegetation sampling in such places presents considerable technical challenges such as equipment failures and exposure to hazardous elements. Controlling aerial manipulation in unstructured areas such as forests remains a significant challenge because of uncertainty, complex dynamics, and the possibility of collisions. To overcome these issues, we offer a new image-based visual servoing (IBVS) method that uses knowledge distillation to provide robust, accurate, and adaptive control of the aerial vegetation sampler. A convolutional neural network (CNN) from a previous study is used to detect the grasp point, giving critical feedback for the visual servoing process. The suggested method improves the precision of visual servoing for sampling by using a learning-based approach to grip point selection and camera calibration error handling. Simulation results indicate the system can track and sample tree branches with minimum error, demonstrating that it has the potential to improve the safety and efficiency of aerial vegetation sampling.