Multi-axis System Research Articles

A novel sequential observability analysis method for a multi-FOG inertial navigation system

Abstract The Multi-FOG inertial navigation system (INS), especially for the six-axis FOG INS with the regular dodecahedron configuration, has been widely used in high-reliability navigation. However, observability analysis of the system is still a severe challenge because the traditional method is focused on the orthogonal coordinate system, leading the rank analysis method is unsuitable for the multi-FOG system. In present study, we take the six-axis system as an example and propose a novel sequential observability analysis method to solve the problem. By making full use of the multi-coordinate characteristics of the system, the observability analysis is transformed into a problem that can be solved directly by twenty coordinate systems, which completed the observability characteristics of the system in static environment. Moreover, the hardware-in-the-loop simulation and the covariance analysis are employed to verify the observability conclusion. The results show that the bias errors of the FOGs can be observed in static environment, and all bias converge within 1800 seconds. The convergence accuracies of the FOGs are about 15.3%, 15.6%, 23.3%, 28.6%, 29.3% and 29.6%, respectively. Compared with the existed methods, our method makes full use of the multiple coordinates property to complete the observability characterization, exhibiting the merits of simplicity, validity, and stability, which is expected to be widely used in non-orthogonal multi-axis system.

Polarization grating based circular subaperture stitching interferometer insensitive to vibration and alignment error

The subaperture stitching interferometry can extend the dynamic range of the interferometer and is widely used for high-resolution measurement of large-aperture, high-slope aspheres. Most subaperture stitching interferometry requires a high-precision multi-axis motion control system and a vibration-free environment. This paper proposes a polarization grating-based circular subaperture stitching interferometer assisted by a virtual-real combination algorithm to realize non-null measurement insensitive to vibration and alignment error. A polarization grating that scans subapertures through its axial rotation is adopted in place of the multi-axis motion control system. Polarization phase-shifting interferometry is introduced to achieve transient measurement of a single subaperture. Compared with traditional subaperture stitching interferometers, the proposed system is more compact and reduces measurement errors introduced by mechanical adjustments, improving the insensitivity to the vibration of individual subaperture measurements. Additionally, a virtual-real combination algorithm effectively suppresses retrace errors, reduces the impact of alignment errors on measurement accuracy, improves the overall insensitivity to vibration of subaperture stitching, and avoids positioning and calculation errors caused by complex stitching algorithms. The feasibility of the system and method, as well as their merits in insensitivity to vibration and large alignment tolerance, are verified through experiments. This research provides innovative and instructive insights into the application of polarization grating-based circular subaperture stitching interferometers.

Precision Coordinated Control of Gantry Multi-Axis Systems With Coupled Dynamics and Prescribed Performance

Precision Coordinated Control of Gantry Multi-Axis Systems With Coupled Dynamics and Prescribed Performance

A comparison of near-field to far-field transformation techniques for use with industrial multi-axis robotic antenna measurement systems

Abstract This invited, extended, paper compares and contrasts a number of different near-field (NF) to far-field (FF) transformation algorithms that can be used for the purpose of processing NF data acquired using multi-axis industrial robots. The merits and limitations of these various, commonly encountered algorithms are highlighted with comparison FF data presented across a frequency range spanning 3–15 GHz. Crucially, the paper explores the viability of using mixed mode acquisition geometries when performing antenna gain measurements where, prior to this work, several of the transforms yielded different transform gains, and electrical lengths. Here, we verify that at 8 GHz and above, where truncation effects were minimal, for a circa 30 dBi gain (at 8 GHz) test antenna the FF peaks were in agreement to better than ±0.02 dB, at 3σ irrespective of the acquisition geometry and transform algorithm used. In this invited, extended work, the existing simulation results are augmented with experimental results obtained from planar and spherical NF measurements of a pyramidal horn taken using a dual robotic antenna measurement system and a consistent distributed RF subsystem.

Opto-mechanical error analysis and compensation method for multi-axis laser galvanometer scanning systems

Opto-mechanical error analysis and compensation method for multi-axis laser galvanometer scanning systems

Fault diagnosis for multi-axis carving machine systems with Gaussian mixture hidden Markov models: A data-model interactive perspective

Fault diagnosis for multi-axis carving machine systems with Gaussian mixture hidden Markov models: A data-model interactive perspective

Multi-Axis Servo System Control Design Considering Low-Speed Friction Dynamics

This paper introduces an extended state observer-based command filtered backstepping control method to improve the tracking accuracy and synchronization performance of multi-motor-driving servo systems under the condition of low-speed nonlinear friction dynamics. Firstly, a novel and effective four-motor synchronization scheme is designed and various groups of synchronization feedback signals are introduced to achieve accurate synchronization performance among motors. Then, the observer is designed to estimate the friction torque. And the controller is developed via command filtered backstepping technique to avoid additional computational complexity, making the control signals suitable for practical application. Finally, the stability of the closed-loop system is analyzed, and the tracking and synchronization performance is verified through designed experiments.

A high-precision trajectory capture and playback solver for KUKA iiwa robot

AbstractThis paper introduces a sophisticated trajectory capture and playback mechanism for collaborative robots, aimed at enhancing accuracy and operational efficiency through several innovative techniques. The Ju-Gibbs attitude quaternion is utilized for enhanced kinematic modeling across multi-axis systems, which simplifies variables, reduces dimensions, and enhances symbolic clarity, thus surpassing the limitations of traditional rotation vectors and unit quaternions. A new sliding filter is developed to effectively reduce noise and optimize trajectory details more efficiently. Additionally, an automated mechanism is implemented for adjusting the sampling rate and removing static data points at the trajectory’s start and end, further refining data collection accuracy. These advancements have been successfully replicated on the Kuka robot LBR iiwa 7 R800, demonstrating the practical applicability of the solutions in real-world settings.

A Method for Generating Toolpaths in the Manufacturing of Orthosis Molds with a Five-Axis Computer Numerical Control Machine

This paper proposes a new algorithm for the automatic generation of toolpaths for machining complex geometric positions, such as molds used in orthosis production. The production of individualized orthoses often requires the use of multi-axis machining systems, such as five-axis machines or industrial robots. Typically, complex and expensive CAD/CAM systems are used to generate toolpaths for these machines, requiring the definition of a machining strategy for each surface. While this approach can achieve a reliable and high-quality machining process, it is very time-consuming and makes it challenging to meet the criteria for rapid production of orthopedic aids. Given that their production is a custom-made process using individual shapes as inputs, the toolpath generation process becomes even more demanding. To address these challenges, this paper proposes an algorithm suitable for the automatic generation of toolpaths for such complex positions. The proposed algorithm has been tested and has proven to be robust and applicable.

Adaptive neural chattering suppression pseudo inverse active control for a class of micromilling systems with multiaxis piezoelectric actuators

Adaptive neural chattering suppression pseudo inverse active control for a class of micromilling systems with multiaxis piezoelectric actuators

Design and validation of 3D self-supporting structures and printing paths for multi-axis additive manufacturing

Design and validation of 3D self-supporting structures and printing paths for multi-axis additive manufacturing

Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms

The integration of technological advancements has significantly impacted artificial intelligence (AI), enhancing the reliability of AI model outputs. This progress has led to the widespread utilization of AI across various sectors, including automotive, robotics, healthcare, space exploration, and defense. Today, air defense operations predominantly rely on laser designation. This process is entirely dependent on the capability and experience of human operators. Considering that UAV systems can have flight durations exceeding 24 h, this process is highly prone to errors due to the human factor. Therefore, the aim of this study is to automate the laser designation process using advanced deep learning algorithms on 3D point clouds obtained from different sources, thereby eliminating operator-related errors. As different data sources, dense 3D point clouds produced with photogrammetric methods containing color information, and point clouds produced with LiDAR systems were identified. The photogrammetric point cloud data were generated from images captured by the Akinci UAV’s multi-axis gimbal camera system within the scope of this study. For the point cloud data obtained from the LiDAR system, the DublinCity LiDAR dataset was used for testing purposes. The segmentation of point cloud data utilized the PointNet++ and RandLA-Net algorithms. Distinct differences were observed between the evaluated algorithms. The RandLA-Net algorithm, relying solely on geometric features, achieved an approximate accuracy of 94%, while integrating color features significantly improved its performance, raising its accuracy to nearly 97%. Similarly, the PointNet++ algorithm, relying solely on geometric features, achieved an accuracy of approximately 94%. Notably, the model developed as a unique contribution in this study involved enriching the PointNet++ algorithm by incorporating color attributes, leading to significant improvements with an approximate accuracy of 96%. The obtained results demonstrate a notable improvement in the PointNet++ algorithm with the proposed approach. Furthermore, it was demonstrated that the methodology proposed in this study can be effectively applied directly to data generated from different sources in aerial scanning systems.

An innovative joint-space dynamic theory for mobile multi-axis system with unilateral constraint

An innovative joint-space dynamic theory for mobile multi-axis system with unilateral constraint

Active suspension and steering system control of emergency rescue vehicle based on sliding mode dual robust coordination control

The multi-axle emergency rescue vehicle has dangerous driving conditions, high body height, and large weight, and the coupling of each subsystem of the chassis is more complicated. In order to solve the coordination problem between the vehicle suspension system and the steering system, the vehicle can drive more smoothly on uneven road surfaces and improve the riding comfort of passengers. A sliding mode dual robust coupled collaborative control strategy is proposed to address the impact of the multi-axis steering system and active suspension system on driving smoothness and handling stability of emergency rescue vehicles. The active suspension system and multi-axle steering system are synergistically controlled. Firstly, an active suspension sliding mode variable structure controller is designed to improve vehicle ride comfort. Secondly, A new dual robust controller is proposed to realize the handling stability of the vehicle’s all-wheel steering system. Thirdly, the coupling cooperative controller of the active suspension system and all-wheel steering system is designed and simulated under different working conditions. The experimental results show that the root-mean-square values of body roll angle, body roll angle acceleration, and yaw angle acceleration with cooperative control are reduced by 26.58%, 30.54%, and 21.92% respectively; moreover, the lateral acceleration and vertical acceleration of the vehicle body are effectively reduced. The use of cooperative control effectively improves the ride comfort and handling stability of the vehicle.

Trajectory optimization of B-splines interpolation based on dynamic error adjustment

In this paper, we propose a motion planning algorithm based on the five-fold B-spline curve for multi-axis motion control systems with non-smooth trajectories, acceleration, plus acceleration over the limit, etc. We construct a smooth transition model for the micro-line segment G3 of the continuous five-fold B-spline curve. A derivation of the corresponding theoretical equation is given. Using the transition model, we propose an optimization method for dynamic adjustment of motion trajectory errors based on the velocity forward planning constraints by smoothing the transition after the trajectory of the micro-line segment for the forward planning velocity. Finally, the design flow for the forward planning of the velocity is given. Finally, the above algorithm is used to perform simulation experiments for the optimization algorithm. Experimental results show that the proposed optimization improves the accuracy of fitting motion trajectories under the constraints of acceleration and deceleration addition, which validates the proposed algorithm.

Design and test of an efficient seedling pick-up device with a combination of air jet ejection and mechanical action

Low degree of transplanting automation will affect production efficiency and planting quality in vegetable cultivation. A new seedling pick-up device was designed and constructed to reduce direct grasping damages to seedlings and improve transplanting efficiency. The pick-up device consists of an air jet loosening device, a flexible pick-up manipulator, a parallel feeding device, and a multi-axis motion control system. Its working principle is to use air jet ejection to assist in loosening of seedling roots from the tray cells, grasp their stems for extracting with the pick-up manipulator, and finally transfer them to the delivery device for feeding into the planting device as needed. The mechanical structure and working parameters of each component were designed, and the control system was constructed according to the working requirements of ejecting, extracting, transferring, and discharging operations. A prototype of the new pick-up device was constructed, and its performance evaluation was conducted using an orthogonal experimental design using cucumber, pepper and caluiflower as test objects. The results showed that the test object, the root lump's moisture content and the loosening way (either as a whole or individual loosening of seedlings) had significant effects on the success ratio in picking up seedlings. Overall, the success in picking up seedlings from the cell was found to be influenced by horticultural and mechanical factors. Under the optimal level group, the maximum success ratio for automatic picking up seedlings was up to 94.49% for pepper while that of cucumber and cauliflower recorded 90.75% and 92.62%, respectively. The seedling pick-up performance was satisfactory for efficient transplanting requirements.

Supportless 3D-printing of non-planar thin-walled structures with the multi-axis screw-extrusion additive manufacturing system

Supportless 3D-printing of non-planar thin-walled structures with the multi-axis screw-extrusion additive manufacturing system

An adaptive feed-forward active vibration isolation algorithm for input disturbance unbalanced MIMO systems

An adaptive feed-forward active vibration isolation algorithm for input disturbance unbalanced MIMO systems

Detecting vibrations in digital holographic multiwavelength measurements using deep learning.

Digital holographic multiwavelength sensor systems integrated in the production line on multi-axis systems such as robots or machine tools are exposed to unknown, complex vibrations that affect the measurement quality. To detect vibrations during the early steps of hologram reconstruction, we propose a deep learning approach using a deep neural network trained to predict the standard deviation of the hologram phase. The neural network achieves 96.0% accuracy when confronted with training-like data while it achieves 97.3% accuracy when tested with data simulating a typical production environment. It performs similar to or even better than comparable classical machine learning algorithms. A single prediction of the neural network takes 35µs on the GPU.

Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control

With the widespread application of multi-axis machining in the industrial manufacturing, aerospace, and military equipment sectors, the demand for machining ultra-precision components has been steadily increasing. Contour errors directly impact the quality of machined parts. In conventional multi-axis motion control systems based on cross-coupling, it is conventionally assumed that all individual axes are of equal significance during machine processing. However, in practical machining scenarios, diverse machining trajectories and accuracy requirements give rise to distinct control necessities for each axis. This complication leads to challenges in ensuring a consistent single-plane contour, thereby constraining the elevation of the overall contour accuracy. To address this issue, this study proposes a multi-axis contour error suppression method based on composite hierarchical control. The approach advocated in this paper initially ensures the precision of single-axis position control through the development of an advanced S-shaped function-based sliding-mode disturbance observer. Building on this foundation, the three-dimensional spatial contour is segregated into upper and lower layers. Subsequently, dedicated fuzzy PID cross-coupling controllers are devised for each layer. The experimental outcomes substantiate that in comparison to conventional cross-coupling control methods, the method introduced in this study, rooted in composite hierarchical control, not only guarantees the accuracy of single-plane contours but also further enhances the overall contour precision.