Maximum Position Error Research Articles

Research on Some Control Algorithms to Compensate for the Negative Effects of Model Uncertainty Parameters, External Interference, and Wheeled Slip for Mobile Robot

In this article, the research team systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These control algorithms were developed based on dynamic surface control (DSC). An adaptive trajectory tracking DSC algorithm using a fuzzy logic system (AFDSC) and a radial neural network (RBFNN) with a fuzzy logic system were used to overcome the disadvantages of DSC and expand the application domain for non-holonomic wheeled mobile robots with lateral slip (WMR). However, this adaptive fuzzy neural network dynamic surface control (AFNNDSC) adaptive controller ensures the closed system is stable, follows the preset trajectory in the presence of wheel slippage model uncertainty, and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on the Matlab/simulink software R2022b and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the theoretical analysis’ correctness, the proposed controller’s effectiveness, and the possibility of practical applications. Orbits are set as two periodic functions of period T as follows.

Physics-informed neural networks for heat transfer prediction in two-phase flows

This paper presents data-driven simulations of two-phase fluid processes with heat transfer. A Physics-Informed Neural Network (PINN) was applied to capture the behaviour of phase interfaces in two-phase flows and model the hydrodynamics and heat transfer of flow configurations representative of established numerical test cases. The developed PINN approach was trained on simulation data derived from physically based Computational Fluid Dynamics (CFD) simulations with interface capturing. The present study considers fundamental problems, including tracking the rise of a single gas bubble in a denser fluid and exploring the heat transfer in the wake of a bubble rising close to a heated wall. Tracking of a rising bubble phase interface of fluids with disparate properties was performed, revealing a maximum error of only 5.2% at the interface edge and a maximum error of 2.8% at the position of the centre of mass. Inferred (hidden variable) flows are studied in addition to a purely extrapolative inverse isothermal bubble case. When no velocity data was supplied, velocity field predictions remained accurate. Rise of an inferred isothermal bubble with unseen fluid properties was found to produce a maximum mean-squared error of 0.28 and centre of mass error of 1.25%. For the case of the rising bubble with a hot wall, the maximum error in the temperature domain using specified boundary conditions was 6.8%, while the bubble position analysis reveals a maximum positional error of 3.6%. These results demonstrate that PINN is agnostic to geometry and fluid properties when studying the combined effects of convection and buoyancy on two-phase flows for the first time. This work serves as a starting point for PINN in multiphase cases involving heat transfer over a range of geometries. Eventually, PINN will be used in such cases to provide solutions for forward, inverse, and extrapolative cases. Each of which represent a dramatic saving in computational cost compared to traditional CFD.

Snake optimizer LSTM-based UWB positioning method for unmanned crane.

Position determination is a critical technical challenge to be addressed in the unmanned and intelligent advancement of crane systems. Traditional positioning techniques, such as those based on magnetic grating or encoders, are limited to measuring the positions of the main carriage and trolley. However, during crane operations, accurately determining the position of the load becomes problematic when it undergoes swinging motions. To overcome this limitation, this paper proposes a novel Ultra-Wide-Band (UWB) positioning method for unmanned crane systems, leveraging the Snake Optimizer Long Short-Term Memory (SO-LSTM) framework. The objective is to achieve real-time and precise localization of the crane hook. The proposed method establishes a multi-base station and multi-tag UWB positioning system using a Time Division Multiple Access (TDMA) combined with Two-Way Ranging (TWR) scheme. This system enables the acquisition of distance measurements between the mobile tag and UWB base stations. Furthermore, the hyperparameters of the LSTM network are optimized using the Snake Optimizer algorithm to enhance the accuracy and effectiveness of UWB positioning estimation. Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.

Multi-detector simultaneous sampling design in the spot-scanning defects detection system

With the decreases in semiconductor critical dimension and increases in circuit intricacy, the impact of wafer defects on the yield and manufacturing cost of integrated circuits is becoming increasingly significant. In this paper, a multimodal spot-scanning defect detection system designed for unpatterned wafer surfaces is introduced. The defect detection system's mechanics, electronics, and software are introduced, focusing on presenting a multi-detector simultaneous sampling design. The signal sampling is based on the position triggering of the rotary stage encoder. The focused illumination spot is scanned in a helical trajectory relative to the wafer in coordination with the motion of the linear stage and rotary stage. The signal detection is carried out synchronously, and the signal sampling is triggered by rotary stage encoder position pulses to achieve equally spaced sampling. The detector signals are trans-impedance amplified and transmitted differentially to transfer the analogue signals from the optical head to the main control board. Under FPGA control, the main control board completes the simultaneous sampling and signal processing through nine high-precision 16-bit SAR(successive-approximation-register) ADCs(analogue-to-digital converter) under the trigger signal. The defect detection system is designed with a pixel resolution of 4 × 4 μm, requiring a sampling position error of less than 1 pixel and an inter-channel position deviation of less than 0.1 pixel. The rotary stage prototype has a design speed of 120 rpm, and the positioning accuracy of the encoder is ±8 arcsec. Therefore, the maximum sampling position error is 1.94 μm for a 4-inch wafer sample. The maximum phase deviation between the nine channels is 32.5 ns, and the maximum position deviation is 20.4 nm.

High-Precision and Modular Decomposition Control for Large Hydraulic Manipulators

It is difficult to achieve a high-precision motion control in hydraulic manipulators due to their structural redundancy, strong coupling of closed-chain structures, and flow–pressure coupling. In this paper, a high-precision motion control method for hydraulic manipulators is proposed based on the traditional virtual decomposition control (VDC). The method proposed avoids an excessive virtual decomposition of the hydraulic manipulator and requires fewer model parameters than the traditional VDC. Further, the control precision improved by combining an adaptive real-time update of the inertial parameters. Compared with MBC, the proposed control method improved the motion accuracy of the hydraulic manipulator by more than 40% and 20% under elliptical and triangular trajectories. The simulation results showed that the proposed control method reduced the maximum position errors in Cartesian space by 90.4%, 86.8%, 23.6%, and 44.3% compared with PID and model-based control (MBC) in the absence of disturbances. The maximum position error in Cartesian space was reduced by 76.5% compared with that of MBC in a simulation with external disturbances. It can be seen from all the simulation results that with the proposed control method, the position error of the manipulator was less than 50 mm. The proposed control method effectively improved the motion precision of the examined hydraulic manipulator.

Incremental shape measurement with fiber Bragg gratings in a multi-core fiber for three-dimensional paths.

A multi-core fiber (MCF) provides a compact solution for three-dimensional (3D) shape measurement. In this Letter, an incremental shape measurement method for 3D paths is proposed, using an MCF based on fiber Bragg gratings (FBGs). A few FBG sets can iteratively provide plenty of strain information about the 3D path during navigation. The overall continuities of the curvature and torsion are improved based on intensive strain calculations. Meanwhile, the transformation matrix algorithm is used to reconstruct the shape of 3D paths. Dynamic measurement experiments of a seven-core fiber with two FBG sets are carried out to verify the incremental shape measurement method. This method shows a great performance of the different paths, with a maximum incremental position error of 4.68%.

Trajectory planning and control algorithm of industrial robot manipulator

As the industrial robot task becomes more complex, the difficulty of trajectory planning and tracking control of manipulator is gradually increasing. To minimize the vibration during the manipulator motion and improve the planning accuracy, the method of quintic polynomial combined with non-uniform B-spline interpolation is studied for joint space (JS) planning. The trajectory tracking system is easily affected by friction nonlinearity and parameters. So a JS trajectory tracking controller based on based on fuzzy neural network (FNN) is designed. Through simulation experiments, the curve obtained by the planning method studied is smoother and the planning error is minimum. The maximum position error is 0.09 rad, and the speed error is not more than 0.1 rad/s. The controller performance test results under different parameters show that the W^, c^, κ^ parameter in FNN can be adjusted in real time, and the value will not affect the performance of the controller. The fluctuation range of trajectory error of different joints is within ±0.2×10-5rad, which indicates that the performance of AFNNC controller studied is better. And its response time is the shortest and its robustness is better when the load changes suddenly.

A New Error Compensation Method for Delta Robots Combining Geometric Error Modeling With Spatial Interpolating

Abstract Robots are playing an important role in precision manufacturing and automated inspection, which places higher demands on the positioning accuracy. The Delta robot is the most widely used parallel robot with high speed and small cumulative error. In this way, improving the positioning accuracy of the Delta robot can expand its application scenarios. According to the parameter mapping relationship, the position error can be divided into geometric error and non-geometric error. Considering the influence of the orientation errors on the actual position of the robot end, an error model to recognize the geometric error is established, and the parameter identification is performed based on an iterative approach. For the non-geometric error, the 3D annular sector grid division method and the interpolation rule are established based on the error similarity. And then, a new error compensation method for Delta robots is proposed by combining geometric error modeling with spatial interpolating. After compensation, the positioning accuracy of the Delta robot is improved significantly. The maximum absolute positioning error of the robot is reduced by 86.08% from 2.084 mm to 0.290 mm, the average absolute positioning error of the robot is reduced by 81.77% from 0.790 mm to 0.144 mm, and the error trend in the workspace is eliminated, so as to enable the expansion of Delta robot applications.

Development of a precision vertical planar stage as a programmable planar artefact

Planar artefacts play the role of calibration references for areal measuring instruments. Due to the need of high accuracy, they are fabricated by high precision processes. There are various artefacts for different measuring systems. If those planar artefacts can be generated by respective computer programs and performed through a common platform, it could be a versatile and time-saving tool. The goal of this work is to build up a precision vertical planar stage to provide programmable planar artefacts for field accuracy calibration of areal sensors. In order to improve the real-time motion accuracy of the planar stage, its spatial positional errors are modeled, measured, and compensated. The Vector Transfer (VT) method is used to derive the planar positional error model based on Abbe principle and Bryan principle. A new six-degree-of-freedom (6-DOF) measurement system is proposed for simultaneously measuring all geometric errors of the linear stage. Experimental verification by comparing the positioning accuracy of two diagonals of the constructed plane with the commercial laser interferometer has confirmed that the maximum spatial positioning error of the developed vertical stage was reduced from −124.0 μm to 13.5 μm after the positional error compensation, achieving 89.1% accuracy improvement. This vertical planar stage can generate any shape of programmed trajectory for the accuracy calibration of industrial areal sensors.

Optimization of working position and posture of a 5-DOF hybrid automatic drilling system based on an improved GA-BP neural network

ABSTRACT After compensation for positioning error, such errors become different in multipose space. To reduce the positioning error of a 5-degree-of-freedom automatic drilling system, an optimization algorithm is used to optimize the drilling position and posture. The forwards kinematics of the automatic drilling system were determined by a vector cross product. Combined with structural deformation calculation, the workspace of the drilling system was analysed. The pose errors in multipose space were calculated. A BP neural network was used to establish the mapping between the target pose and the pose error after error compensation, and the improved genetic algorithm was used to optimize the working pose. To verify the robustness of the proposed method, drilling comparison experiments were carried out. After optimization, the maximum position and posture error of the parallel posture alignment mechanism were reduced by 71.11% and 67.57%, respectively. The maximum position and normal error of the automatic drilling system were reduced by 54.37% and 19.64%, respectively. Drilling experiments show that the proposed algorithm can meet the accuracy requirements of aircraft assembly fields. The algorithm can be used for working position and posture optimization of a mechanism equipped with posture alignment and an end effector module.

Development of a Novel Ball-and-Socket Flexible Manipulator for Minimally Invasive Flexible Surgery

This work proposes a novel flexible manipulator consisting of a series of 2-DOF vertebrae based on a ball-andsocket joint that is connected by a ball-shaped surface and a cupshaped socket and constrained by pins for circumferential rotation. This manipulator can demonstrate outstanding torsional stiffness since the circumferential rotation between the vertebrae is constrained by four ball pins. The point contact between ball pins and guideways effectively reduces the friction between the vertebrae, thus allowing the designed manipulator to yield a smooth bending shape with constant curvature. This manipulator features high axial and torsional stiffness, excellent bending performance, sufficient loading capacity, and convenient integration with surgical instruments. Moreover, the excellent torsional stiffness enables this manipulator to efficiently transfer torque and be applied in in-situ torsional motion, effectively addressing the typical issue of limited dexterity for torsional motion. The kinematic modeling of the proposed manipulator under in-situ torsional motion has been derived, and its workspace has been analyzed. A robotic system has been assembled, and experiments have verified the proposed design and modeling validity. The results show that the maximum position errors in bending motion are 2.39% (horizontal direction) and 1.98% (vertical direction), and its torsional stiffness is 21.13N∙mm/deg, which is 46 times higher than that of a typical spherical flexible manipulator (SFM). Such merits support this manipulator excellently performing the in-situ torsional motion with a maximum average position error of 3.58%. Furthermore, a phantom test of the larynx has been performed to verify the potential of clinical feasibility.

Error compensation method of industrial robots considering non-kinematic and weak rigid base errors

Industrial robots are receiving increasing attention owing to their numerous advantages, such as flexibility, maneuverability, and low cost. In robotic machining systems, improving the absolute positioning accuracy of the robot is essential for improving workpiece quality. This study proposes a robot error compensation method that considers nonkinematic and weak rigid base errors. A mapping model between the theoretical position of the robot and the actual error was established based on an incremental support vector machine (ISVM), and the nonkinematic error compensation of the robot was realized. The robot error before and after axis 1 rotation is compensated for by modeling the rotational error of axis 1 and the deformation error of the robot base and is verified on the milling robot KUKA KR 160. The experimental results show that the maximum absolute position error of the robot end-effector is reduced by 85.27% (from 1.3277 to 0.1956 mm) after compensation. Milling experiments have shown that the position error of the empty running trajectory before compensation is reduced from −1.3231 to −0.1297 mm, and the z-direction error of the workpiece is reduced from −0.8771 to 0.3267 mm, which meets the machining accuracy requirements of the milling robot system in this paper.

The Maximum Positional Error of Circularly Distributed Holes Drilled with the Assistance of a Post Jig

The Maximum Positional Error of Circularly Distributed Holes Drilled with the Assistance of a Post Jig

Neuromorphic vision based control for the precise positioning of robotic drilling systems

The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for robotic machining applications to enable faster and more reliable operation, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece’s pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling end-effector and drills the target holes on the workpiece using a combined position-based and image-based visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with maximum positional errors of less than 0.2 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution. • We present a neuromorphic visual controller approach for precise robotic machining. • We devise an event-based 3D reconstruction method for the localization of workpieces. • We develop an event-based method for the detection and tracking of circular objects. • We preform experiments that validate the proposed method in a robotic drilling setup. • The presented system can perform drilling tasks with sub-millimeter precision. • Results demonstrate the performance and robustness advantages of neuromorphic vision.

Closed‐Loop Control Characterization of Untethered Small‐Scale Helical Device in Physiological Fluid with Dynamic Flow Rates

Untethered small‐scale helical devices (USHDs) have the potential to navigate blood vessels and treat vascular occlusive diseases. However, there are still many challenges in translating this method into clinical practice, both in terms of localization and wireless motion control. Herein, closed‐loop control characterization of the USHD against and along physiological fluid inside a blood vessel phantom at different penetration depths is shown. First the dynamic flow and ultrasound images noise affecting the measurement are modeled, and control system of the USHD based on bifurcation analysis of a 1D hydrodynamic model is designed. Then a region of attraction of a USHD driven by a permanent magnet robotic (PMR) system inside a blood vessel phantom around an equilibrium point is constructed. Further, the point‐to‐point closed‐loop control strategy is implemented based on the magnetic point‐dipole approximation and kinematic control of the PMR system and ultrasound feedback inside physiological fluid, blood vessel, and soft tissue. The frequency response of the USHD is characterized against and along the flowing streams of fetal bovine serum within different flow rates in the 6–20 mm s−1range. The experimental results demonstrate the ability to navigate the USHD inside blood vessel phantoms with maximum position error of mm.

Multi-Chamber Actuator Using Digital Pump for Position and Velocity Control Applied in Aircraft

This paper presents a multi-chamber hydraulic actuator controlled by digital pumps and on/off valves, in order to improve the efficiency of hydraulic systems applied in aircraft for flight control. Hydraulic positioning systems are used in many different applications, such as mobile machinery, industry and aerospace. In aircraft, the hydraulic actuators are used at flight control surfaces, cargo doors, steering, landing gear and so one. However, the massive use of resistive control techniques, which throttles the passages of the hydraulic fluid, associated with internal leakage of the hydraulic components, make these systems low energy efficient. In order to improve their energy efficiency, digital hydraulics emerges as a promising solution mainly for mobile applications. In this paper a hydraulic positioning system for aircraft control surfaces using a multi-chamber actuator controlled by on/off valves and a digital pump is proposed. The use of a digital pump with three fixed displacement pumps can provide eight different volumetric displacement outputs. The multi-chamber actuator with four areas can operate in two different modes, normal or regenerative, resulting in six different equivalent areas. The regenerative mode allows the actuator to achieve higher actuation velocity values with smaller pumps. These equivalent areas combined with the different supplied flow rates can deliver 43 different discrete output velocity values for the actuator, in steady-state. For the system dynamic analyses, three mathematical simulation models were developed using MATLAB/Simulink and Hopsan, one for the digital system, and two for the conventional solutions applied in aircraft (Servo Hydraulic Actuators (SHA) and Electro Hydrostatic Actuator (EHA)). The simulation results demonstrate that the digital actuator can achieve, for position control, a maximum position error, in a steady-state, of 0.7 mm. From the energy consumption point of view, the digital circuit consumes 31 times less energy when compared with the SHA and 1.7 when compared to the EHA, resulting in an energy efficiency of 54%.

Experimental investigations for the characterisation of an SMA-bias spring actuator in a flexible tube manipulator

The present article describes an application of shape memory alloy (SMA) spring-based actuator for the development of a flexible tube manipulator intended for tumour irradiation application. A constitutive model is proposed that incorporates the nonlinearity of SMA spring along with the loading history of material and predicts 4 N as the force generation capability of the SMA-bias spring actuator. The simulation results follow the characteristic profile of SMA in both normal as well as cyclic loading conditions. Further, experimental characterisation of the actuator is carried out to determine various parameters like modulus of rigidity, critical shear stresses, etc. and a proportional integral derivative (PID) controller is experimentally implemented which made the tip of the tube following a series of predefined points. The maximum positional error is around 5%, which shows the positional tracking ability of the developed manipulator and the maximum bending angle of the tube is 74.6°.

A novel motion-reconstruction method for inertial sensors with constraints

Motion reconstruction for rigid bodies and rigid-body frames using data from inertial measurement units (IMUs) is a challenging task. Position and orientation determination by means of IMUs is erroneous, as deterministic and stochastic errors accumulate over time. The former of which errors can be minimized by standard calibration approaches, however, sensor calibration with respect to a common reference coordinate system to correct misalignment, has not been fully addressed yet. The latter stochastic errors are mostly reduced using sensor fusion. In this paper, we present a novel motion-reconstruction method utilizing optimization to correct measured IMU data by means of correction polynomials to minimize the deviation of motion constraints. In addition, we perform gyrometer and accelerometer calibration with an industrial manipulator to address deterministic IMU errors, especially misalignment. To evaluate the performance of the novel methods, two types of experiments, one at constant orientation and another with simultaneous translation and rotation, were conducted utilizing the manipulator. The experiments were repeated for five individual IMUs successively. Application of the calibration and optimization methods yielded an average decrease of 95% in the maximum position error compared to the results of common motion reconstruction. Moreover, the average position error over the measurement duration decreased by nearly 90%. The proposed method is applicable to velocity, position, and orientation constraints for every experiment that starts and ends at standstill.

A localization method for subsea pipeline based on active magnetization

Accurate location of subsea pipelines is a prerequisite for real-time tracking and detailed inspections by underwater robots. The magnetic anomalies generated by ferromagnetic pipelines can be used to locate both exposed and buried pipelines. However, due to the low signal ratio and model inconsistencies under weak and variable ambient magnetization, there is currently no intuitive and reliable pipeline detection method for pipeline tracking. This paper proposes a method capable of immediately and accurately locating pipelines via active magnetization and vertical magnetic measurements. Finite element simulations show that a magnet array can significantly enhance the magnetic anomaly, and that the vertical magnetic component alone can accurately indicate the pipeline’s position, avoiding the inconvenience of magnetic three-component alignment in the field. It is experimentally demonstrated that the magnetic detection signal-to-noise ratio can be significantly increased by 5 dB–20 dB for a Φ219 mm steel pipe using the magnet array, and the maximum lateral positioning error is 0.03 m and much smaller than that without the magnet array.

An improved path planning algorithm based on artificial potential field and primal-dual neural network for surgical robot

Safety and accuracy are essential for path planning in a surgical navigation system. In this paper, an improved path planning algorithm is proposed to increase the autonomous level of spine surgery robots for higher safety and accuracy. Firstly, the dynamic gravitational constant and piecewise repulsion function are adopted to improve the traditional Artificial Potential Field algorithm to solve the common issues of path planning, including local minimum, unable to reach the target near obstacles. To better control the pose of the end-effector in an operation space, the positions of the two endpoints of the end-effector are further constrained. Secondly, an improved Primal-Dual Neural Network with multiple constraints is proposed to minimize the joint angular velocity norm. The multiple constraints are formulated according to the planned path, the obstacle avoidance of the robot and the joint limits. Moreover, a real-time planned velocity scheme is applied to prevent the accumulation of position errors. The simulation results of the pedicle screw implantation demonstrate that the robot can find the collision-free trajectory and arrive at the target position in various complicated situations. More specifically, the error between two endpoints of the end-effector and the target pose is below 0.1 mm in reaching the surgical tool pose, while the maximum position error is around 0.05 mm when performing the planned path. Moreover, two experiments are conducted in the real-world to verify the proposed algorithm is effective in practice.