Search results
1 – 10 of over 52000The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely…
Abstract
Purpose
The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs.
Design/methodology/approach
In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model.
Findings
Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value.
Originality/value
This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.
Details
Keywords
Pengxin Han, Rongjun Mu and Naigang Cui
The purpose of this paper is to address the flaws of traditional methods and fulfil the special fault‐tolerant re‐entry navigation requirements of reusable boost vehicle (RBV).
Abstract
Purpose
The purpose of this paper is to address the flaws of traditional methods and fulfil the special fault‐tolerant re‐entry navigation requirements of reusable boost vehicle (RBV).
Design/methodology/approach
A kind of improved estimation method based on strong tracking unscented Kalman filter (STUKF) is put forward. According to the fact that the traditional state χ2‐test‐based fault diagnosis method is incompetent to detect the signal point small jerks and slowly varying fault in the measurement, a kind of original fault diagnosis technology based on STUKF is used to check the working states of navigation sensors.
Findings
The comparisons with χ2‐test method under typical failure distributions validate the perfect state tracking and fault diagnosis performances of this improved method.
Practical implications
This kind of state estimation and fault diagnosis method could be used in the navigation and guidance systems for many kinds of aeronautical and astronautical vehicles.
Originality/value
A kind of novel strong tracking state estimation filter is used, and a kind of very effective fault diagnosis criterion is put forward for the navigation of RBV.
Details
Keywords
Janice Aurini and Scott Davies
In this chapter we draw on research from Canada to develop a framework for understanding the variety of forms of supplementary education and their position within broader…
Abstract
Purpose
In this chapter we draw on research from Canada to develop a framework for understanding the variety of forms of supplementary education and their position within broader organization fields of education. The chapter asks: What is the nature and organizing logic of supplementary education in Canada? and, How does supplementary education relate to public schools in Canada?
Design/methodology/approach
Data come from a variety of secondary sources.
Findings
Distributed between three relatively autonomous settings – state, market, and nonprofit – supplementary education exhibits tremendous variety in its use value to parents, instructional content, and organizational form. Supplementary education is popular among Canadian parents and appears to be growing, yet it has failed to fundamentally alter the technical core of Canadian schooling, processes that stratify students, and child and family usage of their time or income. Supplementary education’s inability to penetrate these processes reflects its peripheral position within the broader organizational field of Canadian schooling.
Originality/value
The adoption of an organizational field approach generates new ways of thinking about determinants, forming and organizing logics of supplementary education both nationally and comparatively.
Details
Keywords
Clinton O. Longenecker and Laurence S. Fink
This paper aims to determine the key criteria used by managers in rapidly changing organizations to make promotion decisions.
Abstract
Purpose
This paper aims to determine the key criteria used by managers in rapidly changing organizations to make promotion decisions.
Design/methodology/approach
A survey was conducted of 311 managers from over 100 different US service and manufacturing enterprises experiencing rapid organizational change. Managers were asked to identify no more than five factors that were most critical in their organization to actually getting promoted. Results were content analyzed.
Findings
Top ten factors influencing promotion decisions included: getting desired results/strong performance track record; possessing strong business networks; interpersonal/communication skills; strong knowledge‐experience base; demonstrating a strong work ethic; ability to build teams and being a team player; personality, attitude, and ego factors; solving a major problem or getting a “big hit;” demonstrating character, integrity, and trustworthiness; and, preparation and being in the right place at the right time.
Research limitations/implications
Results indicate what organizations are currently using to make decisions but it does not provide a normative guide for what organizations should be using. Also, further research should attempt to differentiate dimensions used at each level of management.
Practical implications
The results provide a useful guide for managers who are looking for a leg up in the competitive fight for promotions. Results also suggest criteria to be considered when organizations update management assessment tools to better reflect the demands on managers working in the new global business environment.
Originality/value
The study focuses on promotions in rapidly changing organizations and uses a sample that is very familiar with how organizations actually make promotion decisions.
Details
Keywords
Yanbiao Zou and Hengchang Zhou
This paper aims to propose a weld seam tracking method based on proximal policy optimization (PPO).
Abstract
Purpose
This paper aims to propose a weld seam tracking method based on proximal policy optimization (PPO).
Design/methodology/approach
By constructing a neural network based on PPO and using the reference image block and the image block to be detected as the dual-channel input of the network, the method predicts the translation relation between the two images and corrects the location of feature points in the weld image. The localization accuracy estimation network (LAE-Net) is built to update the reference image block during the welding process, which is helpful to reduce the tracking error.
Findings
Off-line simulation results show that the proposed algorithm has strong robustness and performs well on the test set of curved seam images with strong noise. In the welding experiment, the movement of welding torch is stable, the molten material is uniform and smooth and the welding error is small, which can meet the requirements of industrial production.
Originality/value
The idea of image registration is applied to weld seam tracking, and the weld seam tracking network is built on the basis of PPO. In order to further improve the tracking accuracy, the LAE-Net is constructed and the reference images can be updated.
Details
Keywords
Yanbiao Zou, Jinchao Li and Xiangzhi Chen
This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue.
Abstract
Purpose
This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue.
Design/methodology/approach
Robot-based and image coordinate systems are converted based on the mathematical model of the three-dimensional measurement of structured light vision and conversion relations between robot-based and camera coordinate systems. An object tracking algorithm via weighted local cosine similarity is adopted to detect the seam feature points to prevent effectively the interference from arc and spatter. This algorithm models the target state variable and corresponding observation vector within the Bayes framework and finds the optimal region with highest similarity to the image-selected modules using cosine similarity.
Findings
The paper tests the approach and the experimental results show that using metal inert-gas (MIG) welding with maximum welding current of 200A can achieve real-time accurate curve seam tracking under strong arc light and splash. Minimal distance between laser stripe and welding molten pool can reach 15 mm, and sensor sampling frequency can reach 50 Hz.
Originality/value
Designing a set of six-axis robot arm welding seam tracking experiment platform with a system of structured light sensor based on Halcon machine vision library; and adding an object tracking algorithm to seam tracking system to detect image feature points. By this technology, this system can track the curve seam while welding.
Details
Keywords
Fangli Mou and Dan Wu
In recent years, owing to the rapidly increasing labor costs, the demand for robots in daily services and industrial operations has been increased significantly. For further…
Abstract
Purpose
In recent years, owing to the rapidly increasing labor costs, the demand for robots in daily services and industrial operations has been increased significantly. For further applications and human–robot interaction in an unstructured open environment, fast and accurate tracking and strong disturbance rejection ability are required. However, utilizing a conventional controller can make it difficult for the robot to meet these demands, and when a robot is required to perform at a high-speed and large range of motion, conventional controllers may not perform effectively or even lead to the instability.
Design/methodology/approach
The main idea is to develop the control law by combining the SMC feedback with the ADRC control architecture to improve the robustness and control quality of a conventional SMC controller. The problem is formulated and solved in the framework of ADRC. For better estimation and control performance, a generalized proportional integral observer (GPIO) technique is employed to estimate and compensate for unmodeled dynamics and other unknown time-varying disturbances. And benefiting from the usage of GPIO, a new SMC law can be designed by synthesizing the estimation and its history.
Findings
The employed methodology introduced a significant improvement in handling the uncertainties of the system parameters without compromising the nominal system control quality and intuitiveness of the conventional ADRC design. First, the proposed method combines the advantages of the ADRC and SMC method, which achieved the best tracking performance among these controllers. Second, the proposed controller is sufficiently robust to various disturbances and results in smaller tracking errors. Third, the proposed control method is insensitive to control parameters which indicates a good application potential.
Originality/value
High-performance robot tracking control is the basis for further robot applications in open environments and human–robot interfaces, which require high tracking accuracy and strong disturbance rejection. However, both the varied dynamics of the system and rapidly changing nonlinear coupling characteristic significantly increase the control difficulty. The proposed method gives a new replacement of PID controller in robot systems, which does not require an accurate dynamic system model, is insensitive to control parameters and can perform promisingly for response rapidity and steady-state accuracy, as well as in the presence of strong unknown disturbances.
Details
Keywords
Bin Li, Shoukun Wang, Jinge Si, Yongkang Xu, Liang Wang, Chencheng Deng, Junzheng Wang and Zhi Liu
Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random…
Abstract
Purpose
Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.
Design/methodology/approach
To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.
Findings
To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.
Originality/value
This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.
Details
Keywords
– The purpose of this paper is to improve the tracking performance of the tracking loops under high dynamic and severe jamming conditions.
Abstract
Purpose
The purpose of this paper is to improve the tracking performance of the tracking loops under high dynamic and severe jamming conditions.
Design/methodology/approach
First, as the two dominant measurement error sources of the tracking loops, the thermal noise jitter and the dynamic stress error are thoroughly analyzed. Second, a scheme of adaptive tracking loops, which could adaptively adjust the order and the bandwidth of tracking loops, is proposed. Third, real-time detections of the vehicle dynamics and the carrier-to-noise density ratio, and the adaptive bandwidth of the carrier loop are presented, respectively. Finally, simulations are operated to validate the excellent tracking performance of the adaptive tracking loops.
Findings
Based on the principle of minimizing the measurement errors, the loop order and bandwidth are adaptively adjusted in the proposed scheme. Thus, the anti-jamming capability and dynamic tracking performance of the tracking loops could be effectively enhanced.
Practical implications
This paper provides further study on the method of improving the tracking capability under complexly applied conditions of high dynamics and severe jamming.
Originality/value
The detections of carrier-to-noise density ratio and vehicle dynamics are used to adaptively adjusting the loop order and bandwidth, which could not only improve the measurement accuracy but also ensure the stable operation of tracking loops.
Details
Keywords
This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously…
Abstract
Purpose
This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.
Design/methodology/approach
The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.
Findings
Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.
Originality/value
This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.
Details