Real-time Target Research Articles

Large-scale aerial scene perception based on self-supervised multi-view stereo via cycled generative adversarial network

Unmanned aerial vehicle (UAV) has the characteristics of strong maneuverability and wide field of vision, and its application in real-time terrain perception and target capture is one of the active frontier topics in the field of UAV cooperative situation awareness. LiDAR-based method has unique advantages, but data acquisition is difficult and costly. In comparison, multiple view stereo-based (MVS) perception method contains rich image information with low cost, which is relatively easy to be collected. MVS also has potential application value in human–computer interaction and robot positioning (unstructured environment perception, visual servo). To reduce the dependence on costly ground truth, the current self-supervised MVS works calculate the training loss by assuming that the same space points projected into multiple views will share the same RGB information. However, this gap occurs mainly because in the real scene, interference factors such as specular reflection, illumination, and noise will cause large color changes in the source images, which will make it difficult to restrict the photometric consistency of different perspectives with ideal self-supervised loss. To realize more accurate scene perception, a novel self-supervised MVS integrated with generative adversarial network (GAN) is proposed. The overall framework is stacked twice by inputting real images and synthetic images into the network in turn, and adversarial learning is introduced to jointly distinguish the images. In addition, the consistency loss of predicted depth maps from real images and synthetic images is designed to improve the robustness of feature matching and color anti-noise ability. To the best of our knowledge, the proposed work is the first end-to-end depth inference model in MVS field that introduces the adversial training based on GAN, which is an effective complement to self-supervised scene reconstruction. Comprehensive experiment results on the public datasets show that the proposed work is comparable to the mainstream self-supervised MVS.

Towards constructing a DOE-based practical optical neural system for ship recognition in remote sensing images

The optical neural network system based on the 4f system (4f-ONN) is a feasible solution for on-orbit real-time target detection and recognition on remote sensing images, as it can directly modulate and encode the two-dimensional image information. However, traditional 4f systems based on spatial light modulators (SLMs) often encounter misalignment errors during assembly due to the reflective optical path used in SLMs. Additionally, implementing electronic SLM in space-based applications introduces problems such as particle number reversal, significantly reducing the reliability of the system. To address these issues, this paper proposes the adoption of diffractive optical elements (DOEs) to construct the 4f-ONN system. The DOE-based design offers a more compact structure, enhanced reliability, and reduced energy consumption, making it highly suitable for on-orbit image processing applications. Due to the expensive and time-consuming nature of the DOE manufacturing process, a design approach for a 4f system based on DOE was pursued through software simulation in this paper. The simulation phase involved the utilization of electronic neural networks to acquire the physical parameters of the DOE mask, while incorporating the array theorem and Fraunhofer diffraction theorem to accurately calculate the physical dimensions of the DOE. The effectiveness of the adopted DOE-based 4f system was initially validated through experiments on a simple pattern dataset. Subsequently, simulation experiments were conducted on three public datasets, namely Mnist, Fashion-mnist, and QuickDraw16, to confirm the efficacy of the DOE-based 4f system in classification tasks. Lastly, target recognition experiments were performed on the GF-2 dataset, and a corresponding hardware system was developed to demonstrate the potential of the DOE-based 4f system in on-orbit image processing.

Enhancing Image-Guided Radiation Therapy for Pancreatic Cancer: Utilizing Aligned Peak Response Beamforming in Flexible Array Transducers.

To develop ultrasound-guided radiotherapy, we proposed an assistant structure with embedded markers along with a novel alternative method, the Aligned Peak Response (APR) method, to alter the conventional delay-and-sum (DAS) beamformer for reconstructing ultrasound images obtained from a flexible array. We simulated imaging targets in Field-II using point target phantoms with point targets at different locations. In the experimental phantom ultrasound images, image RF data were acquired with a flexible transducer with in-house assistant structures embedded with needle targets for testing the accuracy of the APR method. The lateral full width at half maximum (FWHM) values of the objective point target (OPT) in ground truth ultrasound images, APR-delayed ultrasound images with a flat shape, and images acquired with curved transducer radii of 500 mm and 700 mm were 3.96 mm, 4.95 mm, 4.96 mm, and 4.95 mm. The corresponding axial FWHM values were 1.52 mm, 4.08 mm, 5.84 mm, and 5.92 mm, respectively. These results demonstrate that the proposed assistant structure and the APR method have the potential to construct accurate delay curves without external shape sensing, thereby enabling a flexible ultrasound array for tracking pancreatic tumor targets in real time for radiotherapy.

Abstract 3092: MICRO-TAG: A novel fluorescence-based real-time cellular target engagement platform for drug discovery

Abstract Success of drug discovery programs can be amplified by adopting emerging technologies that can interrogate drug targets within their physiological context. Cell target engagement (CTE) presents a combined biological & biophysical method, which provides quantitative evaluation of target-drug interaction within the native environment of the cell. The currently available luminescence-based CTE methods are limited to measurement of end-point signal and poor resolution, thereby limiting their mechanistic and stoichiometric insight. We have developed MICRO-TAG, a novel fluorescence-based CTE platform, which relies on complementation of split-RNAse A. It enables interrogation of target engagement without interfering with folding, localization and function of target proteins - all within the physiological milieu of the cell. Uniquely, this new platform allows for quantitation and monitoring of CTE in real- time in the cell. It enables extraction of important parameters from target-drug interaction: target thermal profile and temperature of aggregation (Tagg), time required to reach equilibrium (teq), apparent association rate (Kon) and physiologically relevant drug:target binding affinity (KD). We provide data demonstrating potential and utility of MICRO-TAG platform on drug targets, such as KRAS, MTH1, EGFR, and UBE2N. The platform demonstrates relevance for discovery of novel drug candidates targeting challenging drug targets such as transcription factors. This highly scalable and quantitative novel method for assessing drug-target interaction within cells can enhance drug discovery programs, and offers potential to rapidly identify clinically relevant hit compounds for challenging drug targets. Citation Format: Ivan Babic, Nikolas Bryan, Claire Cunningham, Avery Sampson, Daniel Starczynowski, Elmar Nurmemmedov. MICRO-TAG: A novel fluorescence-based real-time cellular target engagement platform for drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3092.

Abstract 2579: A nectin-4 PET radiotracer for pharmacodynamic evaluation of enfortumab vedotin (EV) therapy in urothelial carcinoma

Abstract Introduction: Enfortumab vedotin (EV), an antibody-drug conjugate targeting nectin-4, offers hope for urothelial carcinoma patients (UC) but prolonged use leads to treatment-related toxicities often necessitating dose adjustments. How EV engages nectin-4 at the tumor and its relevance to efficacy is unknown. To understand the pharmacodynamics (target engagement) of EV at the tumor across FDA approved dosing levels, we developed a nectin-4 specific peptide-based radiotracer, [68Ga]AJ647, for positron emission tomography (PET) imaging. Methods: Six UC cell lines of luminal (HT-1376, UC-14, RT-112) and basal (UC-9, SCaBER, T24) morphological type were selected for in vitro and in vivo testing of [68Ga]AJ647 and nectin-4 target engagement by EV. Cell uptake and competitive binding assays were conducted to test [68Ga]AJ647 specificity for nectin-4 in the presence or absence of EV. Flow cytometry derived receptor density was used to correlate [68Ga]AJ647 uptake in cells with nectin-4 expression. Pharmacokinetics, biodistribution and specificity of [68Ga]AJ647 for nectin-4 expression was evaluated in six xenograft models by PET imaging and biodistribution studies. Target engagement by EV was quantified by measuring unoccupied nectin-4 levels by [68Ga]AJ647-PET in HT-1376 xenografts at different doses (15, 9, and 6 mg/kg) and time (1, 3, and 6 days). Results: [68Ga]AJ647 was generated in 20-30% radiochemical yield with >95% purity. In vitro assays showed distinctive uptake in cell lines (HT-1376 > UC-14 > RT-112; UC-9 > SCaBER > T24) that correlated with nectin-4 density (R2=0.85). PET and biodistribution studies showed tractable pharmacokinetics of [68Ga]AJ647 enabling high contrast images in 60 min and exhibiting nectin-4 dependent uptake in all the six xenografts. Uptake of [68Ga]AJ647 in UC cells reduced in a dose dependent manner in the presence of EV, indicating that [68Ga]AJ647 measures unoccupied nectin-4, and paving the way for EV target engagement evaluation. For target engagement evaluation, [68Ga]AJ647 accumulation in HT-1376 tumors in saline treated mice was assumed as 100% unoccupied nectin-4 levels. In mice treated with EV for 24 hrs (n=3-5), unoccupied nectin-4 levels were 21.1±11.6, 23.2±14.5, and 24.3±18.8 for 15, 9, and 6 mg/kg doses, respectively. Whereas on day 6, unoccupied nectin-4 levels showed significant differences at 29.5±15.8, 64.2±21.0, and 89.5±18.9 for 15, 9, and 6 mg/kg dose, respectively. These data indicate that FDA approved doses of EV show higher target engagement at 24 h but a dramatic reduction at lower doses over time. Conclusion: [68Ga]AJ647-PET quantifies variable nectin-4 expression in tumors. Furthermore, [68Ga]AJ647-PET quantifies EV therapy induced pharmacodynamics at the tumor. Such real-time ADC target engagement assessment could inform patient selection and dosing strategies to enhance efficacy and minimize toxicities. Citation Format: Akhilesh Mishra, Ajay K. Sharma, Kuldeep Gupta, Burles A. Johnson, David J. McConkey, Jeannie Hoffman-Censits, Sridhar Nimmagadda. A nectin-4 PET radiotracer for pharmacodynamic evaluation of enfortumab vedotin (EV) therapy in urothelial carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2579.

Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle.

Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target's position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR's real-time point cloud data are filtered based on position-direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target's position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function.

Adaptive prescribed performance based on recursive nonsingular terminal sliding mode control for quad-rotor systems under uncertainty and disturbance: Real-time validation

In this paper, at the aim of the fast trajectory-following control of the Unmanned Aerial Vehicle (UAV) systems subject to uncertainty and disturbance, the adaptive prescribed performance control based recursive nonsingular terminal sliding mode control is suggested. Afterward, for the fast trajectory-following control of the uncertain and perturbed quad-rotor system, a recursive nonsingular terminal sliding mode strategy is recommended. Since the better transient and steady-state response of the sliding mode surface is of the utmost importance in the design of the controller, the prescribed performance control scheme is proposed in which the transformed prescribed form of the previously proposed recursive nonsingular terminal sliding surface is obtained to prove that the recursive nonsingular terminal sliding surface can converge to a preset region. Whereas unknown bounded uncertainty and disturbance always exist in the UAV system's dynamical model which decreases its performance and efficiency, the adaptive mechanism is applied to approximate uncertain bounds of uncertainty and disturbance to advance the implementation of the UAV system. Later, quick reachability of the prescribed form switching surface is demonstrated using Lyapunov concept. Ultimately, simulation results using MATLAB/Simulink over a quad-rotor system and real-time simulation using the Speedgoat Real-Time Target Machine platform are conducted to validate the feasibility and performance of the proposed method.

Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review.

As one of the main treatment modalities, radiotherapy (RT) (also known as radiation therapy) plays an increasingly important role in the treatment of cancer. RT could benefit greatly from the accurate localization of the gross tumor volume and circumambient organs at risk (OARs). Modern linear accelerators (LINACs) are typically equipped with either gantry-mounted or room-mounted X-ray imaging systems, which provide possibilities for marker-less tracking with two-dimensional (2D) kV X-ray images. However, due to organ overlapping and poor soft tissue contrast, it is challenging to track the target directly and precisely with 2D kV X-ray images. With the flourishing development of deep learning in the field of image processing, it is possible to achieve real-time marker-less tracking of targets with 2D kV X-ray images in RT using advanced deep-learning frameworks. This article sought to review the current development of deep learning-based target tracking with 2D kV X-ray images and discuss the existing limitations and potential solutions. Finally, it also discusses some common challenges and potential future developments. Manual searches of the Web of Science, and PubMed, and Google Scholar were carried out to retrieve English-language articles. The keywords used in the searches included "radiotherapy, radiation therapy, motion tracking, target tracking, motion estimation, motion monitoring, X-ray images, digitally reconstructed radiographs, deep learning, convolutional neural network, and deep neural network". Only articles that met the predetermined eligibility criteria were included in the review. Ultimately, 23 articles published between March 2019 and December 2023 were included in the review. In this article, we narratively reviewed deep learning-based target tracking with 2D kV X-ray images in RT. The existing limitations, common challenges, possible solutions, and future directions of deep learning-based target tracking were also discussed. The use of deep learning-based methods has been shown to be feasible in marker-less target tracking and real-time motion management. However, it is still quite challenging to directly locate tumor and OARs in real-time with 2D kV X-ray images, and more technical and clinical efforts are needed. Deep learning-based target tracking with 2D kV X-ray images is a promising method in motion management during RT. It has the potential to track the target in real time, recognize motion, reduce the extended margin, and better spare the normal tissue. However, it still has many issues that demand prompt attention, and further development before it can be put into clinical practice.

Hardware-Accelerated YOLOv5 Based on MPSoC

This paper details the development of a hardware acceleration system for YOLOv5, focusing on flame detection as its primary application. The implementation leverages the APU and DPU functionalities integrated into the Zynq UltraScale+ MPSoC XCZU7EV core. The proposed solution addresses the challenge of achieving real-time target detection on mobile terminals, ensuring both real-time operation and ultralow power consumption of YOLOv5. Notably, our design approach facilitates the deployment of all target detection algorithms under TensorFlow for mobile devices. To optimize model efficiency, we employ saturated linear mapping quantization with calibration. This technique maps model weights, double bases, and activations from 32-bit to 8-bit, incurring only a 1.64% accuracy loss. The data flow design is realized through efficient data exchange between DDR, APU, and DPU, utilizing the AXI4 bus architecture. Image pre-processing and post-processing tasks are executed on the APU, while neural network inference occurs on the DPU. Our accelerated system demonstrates compelling experimental results: maintaining a detection speed of 56FPS, achieving an accuracy of 36.56% on the COCO2014 dataset, and exhibiting a total system power consumption of only 4.147W. Furthermore, the energy consumption ratio is measured at 15.41GOPS/W, surpassing the RTX A6000 graphics card by a factor of 55.

A Rapid Control Prototyping and Hardware-in-the Loop Approach for Upper Limb Robotic Exoskeletons Control

In the last decade, robotic-mediated rehabilitation has emerged as a potential solution to improve repetitive task training. Each device in this field has a unique development history shaped by engineers’ expertise in specific programming languages or platforms. In this work we adopt an approach that tries to abstract from the final implementation with the aim to make control logic more shareable and understandable. The authors will present the outcomes of the application of a Rapid Control Prototyping strategy to an upper-limb robotic exoskeleton. A model-based design approach implemented on a real-time target machine is presented. This modern design approach was explored with several control strategies and was used to test the exoskeleton’s performances. The proposed method highlights how it is possible to develop the entire control architecture in a single programming environment.

Improved small-object detection using YOLOv8: A comparative study

In the last decade or so, deep neural networks have evolved at a rapid pace, where computer vision has been constantly refreshing its best performance and has been integrated into our lives. In the field of target detection, YOLO model is a popular real-time target detection algorithm model that is fast, efficient, and accurate. This research aims to optimize the latest YOLOv8 model to improve its detection of small objects and compare it with another different version of YOLO models. To achieve this goal, we used the classical deep learning algorithm YOLOv8 as a benchmark and made several improvements and optimizations. We optimized the definition of the detection head, narrowed its perceptual field, and increased its number, allowing the model to better focus on the detailed information of small objects. We compared the optimized YOLOv8 model with other classical YOLO models, including YOLOv3 and YOLOv5n. The experimental results show that our optimized model improves small object detection with higher accuracy. This research provides an effective solution for small object detection with good application prospects. With the continuous development and improvement of the technology, we believe that the YOLO algorithm will continue to play an essential role in object detection and provide a reliable solution for various real-time applications.

Military target detection method based on EfficientDet and Generative Adversarial Network

Military target identification is one of the first tasks of modern counter-terrorism operations, and military target detection methods based on unmanned system platforms can effectively reduce personnel casualties and improve combat effectiveness. Due to the complexity and variability of the actual combat environment and the demand for real-time target recognition, this paper proposes an image recognition method based on the combination of EfficientDet and Generative Adversarial Network (GAN), in which the gauged image features extracted from the EfficientDet model are used as the input of the GAN for the game learning of image categories and features. The learning results are also used as the feature reuse input of the EfficientDet for feature learning, so that this recognition model can obtain higher recognition speed and recognition accuracy. Through model testing with embedded experiments on unmanned platforms, the results show that the network has a higher mean-Average-Precision compared to the traditional one-stage approach, while improving the recognition accuracy of targets in different complex environments while maintaining no significant reduction in Frames Per Second.

Deep learning-assisted 3D laser steering using an optofluidic laser scanner.

Laser ablation is an effective treatment modality. However, current laser scanners suffer from laser defocusing when scanning targets at different depths in a 3D surgical scene. This study proposes a deep learning-assisted 3D laser steering strategy for minimally invasive surgery that eliminates laser defocusing, increases working distance, and extends scanning range. An optofluidic laser scanner is developed to conduct 3D laser steering. The optofluidic laser scanner has no mechanical moving components, enabling miniature size, lightweight, and low driving voltage. A deep learning-based monocular depth estimation method provides real-time target depth estimation so that the focal length of the laser scanner can be adjusted for laser focusing. Simulations and experiments indicate that the proposed method can significantly increase the working distance and maintain laser focusing while performing 2D laser steering, demonstrating the potential for application in minimally invasive surgery.

Optimizing Controls to Track Moving Targets in an Intelligent Electro-Optical Detection System

Electro-optical detection systems face numerous challenges due to the complexity and difficulty of targeting controls for “low, slow and tiny” moving targets. In this paper, we present an optimal model of an advanced n-step adaptive Kalman filter and gyroscope short-term integration weighting fusion (nKF-Gyro) method with targeting control. A method is put forward to improve the model by adding a spherical coordinate system to design an adaptive Kalman filter to estimate target movements. The targeting error formation is analyzed in detail to reveal the relationship between tracking controller feedback and line-of-sight position correction. Based on the establishment of a targeting control coordinate system for tracking moving targets, a dual closed-loop composite optimization control model is proposed. The outer loop is used for estimating the motion parameters and predicting the future encounter point, while the inner loop is used for compensating the targeting error of various elements in the firing trajectory. Finally, the modeling method is substituted into the disturbance simulation verification, which can monitor and compensate for the targeting error of moving targets in real time. The results show that in the optimal model incorporating the nKF-Gyro method with targeting control, the error suppression was increased by up to 36.8% compared to that of traditional KF method and was 25% better than that of the traditional nKF method.

ACCURACY OF A FRAMELESS OPTICAL STEREOTACTIC NEURONAVIGATION SYSTEM FOR BRAIN BIOPSY IN CATS: A CADAVERIC STUDY

Image-guided stereotactic techniques can display the locations of surgical instruments and biopsy targets in real time, allowing minimally invasive brain biopsies with reduced risk. This study aimed to assess the accuracy of an optical frameless neuronavigation system in feline cadavers and describe potential influencing factors. Phantom lesions were created in different brain regions and depths in 15 feline cadavers. After installing eight fiducial markers in the frontal bone and zygomatic arches, magnetic resonance imaging (MRI) was performed, and the images were imported into the neuronavigation system. Following patient and instrument registration, diluted gadolinium was injected into the phantom lesion (target), guided by the neuronavigation system. The coordinates of the target and contrast points were determined on repeated MRI scans and the distance between them was defined as the targeting error. The time from target selection to the completion of gadolinium injection was recorded as the required time. The targeting error and required time were compared between the initial 30% and final 30% of the collected data to assess the influence of operator experience. Among 76 lesions, the mean targeting error was [Formula: see text][Formula: see text]mm (95% confidence interval 2.76–3.15[Formula: see text]mm). Lesion location, lesion depth, and operator experience did not affect the accuracy; however, operator experience reduced the required time. This frameless optical neuronavigation system is potentially feasible for feline brain biopsy. The demonstrated accuracy substantiates its clinical utility for feline brain lesions [Formula: see text][Formula: see text]mm, and is comparable to that of other neuronavigation systems described in dogs.

A novel wall parameter estimation approach based on transmission line method from through-wall radar measurements

A novel technique for estimation of the wall’s relative permittivity, thickness and conductivity from signals measured from the through-wall radar is presented in this paper. The retrieval process is built around an electromagnetic circuit model that represents the wall structure as a transmission line segment. The propagation of the radar signal through the wall is equivalently described as a pair of voltage and current waves that propagate along the transmission line, using analogies between the electrical parameters of the transmission line segment and the constituent parameters of the materials of the wall structure. The synthetic time domain signal obtained analytically and the real through wall imaging time domain signal were correlated to estimate the wall parameters of the layers that comprise the wall structure. The effectiveness of the proposed estimation technique has been demonstrated through simulation and experimentation on two actual building walls. A comparison with conventional methods revealed consistent results between the estimated wall parameters using the proposed technique and those obtained through traditional approaches. These findings highlight the potential of the proposed method in enhancing the accuracy of real-time target localization and tracking in through-wall imaging applications.

A Hybrid BCI Integrating EEG and Eye-Tracking for Assisting Clinical Communication in Patients With Disorders of Consciousness.

Assessing communication abilities in patients with disorders of consciousness (DOCs) is challenging due to limitations in the behavioral scale. Electroencephalogram-based brain-computer interfaces (BCIs) and eye-tracking for detecting ocular changes can capture mental activities without requiring physical behaviors and thus may be a solution. This study proposes a hybrid BCI that integrates EEG and eye tracking to facilitate communication in patients with DOC. Specifically, the BCI presented a question and two randomly flashing answers (yes/no). The subjects were instructed to focus on an answer. A multimodal target recognition network (MTRN) is proposed to detect P300 potentials and eye-tracking responses (i.e., pupil constriction and gaze) and identify the target in real time. In the MTRN, the dual-stream feature extraction module with two independent multiscale convolutional neural networks extracts multiscale features from multimodal data. Then, the multimodal attention strategy adaptively extracts the most relevant information about the target from multimodal data. Finally, a prototype network is designed as a classifier to facilitate small-sample data classification. Ten healthy individuals, nine DOC patients and one LIS patient were included in this study. All healthy subjects achieved 100% accuracy. Five patients could communicate with our BCI, with 76.1±7.9% accuracy. Among them, two patients who were noncommunicative on the behavioral scale exhibited communication ability via our BCI. Additionally, we assessed the performance of unimodal BCIs and compared MTRNs with other methods. All the results suggested that our BCI can yield more sensitive outcomes than the CRS-R and can serve as a valuable communication tool.

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

TTD-YOLO: A Real-Time Traffic Target Detection Algorithm Based on YOLOV5

TTD-YOLO: A Real-Time Traffic Target Detection Algorithm Based on YOLOV5

Low-consumption stepper motor controller with real-time target position change responsiveness based on field programmable gate array.

In response to the demand for low resource consumption, parallel control, and real-time response to target position changes in precision measurement and manufacturing of multi-axis stepper motor controllers, this paper proposes a field programmable gate array-based method for generating trapezoidal velocity profiles and pulse generation, which can easily keep parallelism and independence during multi-axis control. By avoiding using multiplication and division, this controller not only reduces resource consumption but also enhances the pulse output frequency. To address the real-time responsiveness of the velocity profile generation algorithm to changes in the target position during the control process, the algorithm introduces a novel real-time comparative state transition logic for speed control, which makes it capable of adjusting the acceleration within a single clock cycle, enabling its application in scenarios that require higher levels of real-time performance. Finally, the designed controller is applied to a four-axis positioning system for performance validation.