International Journal of Intelligent Robotics and Applications最新文献

The precise gait phase detection with lightweight equipment under variable conditions is crucial for low limb exoskeleton robots. Therefore, the kinematics and dynamics information are investigated. In this paper, a novel radius-margin-based support vector machine (SVM) model with particle swarm optimization (PSO) in feature space called PSO-FSVM is proposed for gait phase detection. The proposed method addresses the dual objectives of maximizing margin while minimizing radius, employing PSO to fine-tune the parameters of the FSVM. This enhancement significantly bolsters the classification accuracy of the SVM. For the measurement of gait features with a lightweight sensor system, the plantar pressure insoles equipped with flexible and elastic sensors are designed. To evaluate the effectiveness of our method, we conducted comparative experiments, pitting the proposed PSO-FSVM against other support vector machine variants, across four treadmill speeds. The experimental results indicate that the proposed method achieves an accuracy of over 98% at four different speeds indoors. Furthermore, the proposed method is compared with other algorithms (SVM, k-nearest neighbor (KNN), adaptive boosting (AdaBoost), and quadratic discriminant analysis (QDA)) under outdoor experiments. The experimental results demonstrate that the average recognition accuracy of this method reaches 96.13% under variable speed conditions, with an average accuracy of 98.06% under slow walking conditions, surpassing the performance of the above four algorithms.

Mobile robotics has become a well-known research area in healthcare applications; as it defines itself from general robotics, it can move in the surrounding environment which is essential for replicating human abilities. Mobile robots can be utilized in the hospital for health care applications like nursing for doctor assistance and patient monitoring, drug delivery, and teleoperation for contagious diseases. However, mobile robots need unique characteristics, such as the function of locomotion, perception, navigation, and vision systems. The solution and challenge of a mobile robot’s characteristics must be considered when developing a mobile robot. Therefore, they are becoming more autonomous, adaptable to changing situations, and extending their range of applications. This study aimed to investigate the system, which includes both physical robot features (sensors & actuators) and a comparison of different mobile robots in terms of their characteristics and applications in health care. In the coming years, mobile robotics will see increased development, incorporating cognitive architecture, artificial intelligence, speech communication, and affective human–robot interaction. Future healthcare intelligent mobile robots aim to enhance autonomy, communication, data security, and ethical considerations, enhancing patient care, efficiency, and collaboration between medical professionals and technology, shaping the future of healthcare delivery. This review paper presents an overview of the current mobile robot design architecture, which advances the design of the next generation of intelligent mobile robots used in healthcare.

Path planning is an intrinsic component of autonomous robotics, be it industrial, research or consumer robotics. Such avenues experience constraints around which paths must be planned. While the choice of an appropriate algorithm is application-dependent, the starting point of an ideal path planning algorithm is the review of past work. Historically, algorithms were classified based on the three tenets of autonomous robotics which are the ability to avoid different constraints (static/dynamic), knowledge of the environment (known/unknown) and knowledge of the robot (general/model specific). This division in literature however, is not comprehensive, especially with respect to dynamics constraints. Therefore, to remedy this issue, we propose a new taxonomy, based on the fundamental tenet of characterizing space, i.e., as a set of distinct, unrelated points or as a set of points that share a relationship. We show that this taxonomy is effective in addressing important parameters of path planning such as connectivity and partitioning of spaces. Therefore, path planning spaces may now be viewed either as a set of points or, as a space with structure. The former relies heavily on robot models, since the mathematical structure of the environment is not considered. Thus, the approaches used are variants of optimization algorithms and specific variants of model-based methods that are tailored to counteract effects of dynamic constraints. The latter depicts spaces as points with inter-connecting relationships, such as surfaces or manifolds. These structures allow for unique characterizations of paths using homotopy-based methods. The goals of this work, viewed specifically in light with dynamic constraints, are therefore as follows: First, we propose an all-encompassing taxonomy for robotic path planning literature that considers an underlying structure of the space. Second, we provide a detailed accumulation of works that do focus on the characterization of paths in spaces formulated to show underlying structure. This work accomplishes the goals by doing the following: It highlights existing classifications of path planning literature, identifies gaps in common classifications, proposes a new taxonomy based on the mathematical nature of the path planning space (topological properties), and provides an extensive conglomeration of literature that is encompassed by this new proposed taxonomy.

Issues about cycle time optimization is of great importance in the field of automotive production, the industrial robots are widely used in the welding process of automobiles, but there is little research on the optimization of intra station rhythm during the design phase. By conducting research on workstation with industrial robot processing as key process, this paper carries out analysis from the selection of equipment layout within the workstation, planning production rhythm, and the facility performance analysis within the workstation. The finding shows the cycle time within the workstation has been reduced by 12 s. This article aims at improving the rhythm of robotic cells in complex production environment, and raising production efficiency of workstation. The robot path is optimized by using intelligent algorithms, the human machine collaborative work has been validated in virtual scenes, some digital design is adopted for modelling and simulating, the designed workstation has been verified from multiple perspectives, and finally achieve the workstation design of applying industrial robots in the production scenario.

Autonomous robotics has permeated several industrial, research and consumer robotic applications, of which path planning is an important component. The path planning algorithm of choice is influenced by the application at hand and the history of algorithms used for such applications. The latter is dependent on an extensive conglomeration and classification of path planning literature, which is what this work focuses on. Specifically, we accomplish the following: typical classifications of path planning algorithms are provided. Such classifications rely on differences in knowledge of the environment (known/unknown), robot (model-specific/generic), and constraints (static/dynamic). This classification however, is not comprehensive. Thus, as a resolution, we propose a detailed taxonomy based on a fundamental parameter of the space, i.e. its ability to be characterized as a set of disjoint or connected points. We show that this taxonomy encompasses important attributes of path planning problems, such as connectivity and partitioning of spaces. Consequently, path planning spaces in robotics may be viewed as simply a set of points, or as manifolds. The former can further be divided into unpartitioned and partitioned spaces, of which the former uses variants of sampling algorithms, optimization algorithms, model predictive controls, and evolutionary algorithms, while the latter uses cell decomposition and graph traversal, and sampling-based optimization techniques.This article achieves the following two goals: The first is the introduction of an all-encompassing taxonomy of robotic path planning. The second is to streamline the migration of path planning work from disciplines such as mathematics and computer vision to robotics, into one comprehensive survey. Thus, the main contribution of this work is the review of works for static constraints that fall under the proposed taxonomy, i.e., specifically under topology and manifold-based methods. Additionally, further taxonomy is introduced for manifold-based path planning, based on incremental construction or one-step explicit parametrization of the space.

Abstract

Human behavior recognition is a hot research topic in the field of computer vision, and a complete behavior recognition usually includes human detection, human tracking and behavior recognition. At present, the two tasks of human tracking and abnormal behavior recognition based on deep learning are mostly executed separately, and the related feature information in the two tasks cannot be fully utilized, resulting in high time cost and resource consumption of the final abnormal behavior recognition algorithm. The problem greatly limits the widespread application of abnormal behavior recognition. In order to improve the performance of the algorithm a novel model for abnormal behaviors recognition based on human target tracking is proposed, which implements the process of recognizing abnormal behaviors after human target tracking through feature sharing. First, the real-time multi-domain convolutional neural network is improved by introducing a spatial attention mechanism to improve its tracking of a particular human body in a video series. Then the output of the convolutional layer in MDnet is used as the input of the abnormal behavior recognition network, and these features are combined with CNN and LSTM to realize human abnormal behavior recognition. During the network training process, a multi-task learning approach was used to train a model for human tracking and behaviour recognition. Six types of abnormal behaviors selected on the CASIA Behavioural Analytics dataset and 12 types of behaviours selected on the NTU database are used to train and test the network model. According to test results, the proposed model is capable of tracking human targets precisely and in real time (26 frames per second). The proposed model can also distinguish abnormal behaviors of tracking targets with a recognition rate of 92.1%. The human features obtained in the tracking model is used as the input of the abnormal behavior recognition network, so the feature sharing of tracking and recognition is achieved, and a complete abnormal behavior recognition framework including target tracking, feature extraction, and behavior recognition is established. There is great practical significance to the proposed method.

Abstract

Robot-based painting industries optimize operations and enhance product quality by leveraging insights from real and virtual studies, encompassing trajectory patterns, paint film qualities, and machine learning for fault identification. Automation of fault identification procedures is the novel aspect of the study that helps to reduce human error and maintain consistent quality standards in manufacturing. This in-depth investigation examines the analysis of paint paths for robot painting with a focus on three distinctive movement patterns: linear, circular, and zigzag. The investigation includes assessments of smoothness for each route, along with morphological evaluations using Scanning Electron Microscope (SEM) pictures. The surface quality is assessed methodically using Taguchi L9 orthogonal testing, while Analysis of Variance (ANOVA) is utilised to identify the key factors that contribute to variations in paint qualities. In order to enhance quality control, machine learning is included to automate the classification and identification of flaws, utilising sophisticated picture analysis techniques. It is essential to incorporate virtual-environment experiments to ensure the accuracy and applicability of the results in real-world situations. This technique reveals crucial observations on the temporal difference between virtual and real surroundings, providing significant information for enhancing the painting process to better match the actual operational parameters. In addition, the analysis determines that the best combination of roughness is A3B3C2 using the Taguchi method, which results in an outstanding finish with a roughness value of 0.0347 µm. Verifying the efficacy of cutting-edge technology in honing painting techniques and improving end product quality, the machine learning model demonstrates a remarkable 94% accuracy in real-time flaw detection.

At present, most clothing sewing relies on manual labor, and robot sewing has become a trend. However, different clothing styles have various sewing requirements. This poses a challenge for robot sewing, and the key to solving this challenge lies in the planning of robot operation trajectories. Although the shapes of sewing components are diverse, we can decompose them into the most basic straight lines and curved edges. In order to solve the trajectory planning problem in robot sewing process, this paper divides the sewing task into two parts: straight line and curve, and proposes a new robot sewing method based on task process decomposition. Firstly, The robot complex sewing task is divided into two parts: straight line and curve. Based on the extensibility, the sewing tension is predicted, and the robot linear sewing based on impedance control is realized. At the same time, the trajectory planning is carried out on the basis of the line identification of the curved edge to realize the curve sewing. Finally, the robot complex stitch sewing under different curvatures is realized on the built physical experiment platform. It is verified that the effectiveness of the robot sewing method based on process modeling.

A task scheduling and error control optimization method for robotic arms was developed. The arm’s accuracy after optimization with particle swarm optimization, artificial bee colony, grey wolf optimizer, the genetic algorithm, differential evolution algorithm, and the bat algorithm was compared to identify the best optimization method. Task scheduling was optimized by identifying the optimal paths to each target object. The method can control positioning error, enabling the robotic arm to reach its target coordinates with the smallest error despite being affected by interference during navigation. The proposed method was verified in virtual environments with varying target objects at different locations. The estimation results and convergence speed of each algorithm were compared to identify the most accurate algorithm. The proposed method could be used to improve the task scheduling and error control of robotic arms. The method could also be used in combination with algorithms in accordance with the requirements of practical scenarios.

Abstract

Stability and robustness are the important expressions of intelligent walking ability of biped robots. The Zeno behavior caused by the frictional impact of knee joints affects the stability during the dynamic walking, which has greatly limited robot’s application and efficiency. Based on the analysis of the intrinsic mechanism of Zeno behavior, this paper aims to explore biped walking control methods to provide theoretical basis and key technologies for suppressing Zeno behavior. The internal relationship between Zeno behavior and robot knee joint collision is built by studying the cause of Zeno behavior. An event-based feedback controller is proposed to deal with the problem of stabilization of Zeno periodic orbit. It is achieved adaptive periodic stable walking in complex environment based on event-based and hybrid zero dynamic control strategy, which proposes the stability analysis method based on Poincare return map. Meanwhile, the identify parameters of dynamic equations with Zeno behavior is utilized with genetic algorithm and particle swarm optimization. Finally, the effectiveness of the proposed method is verified by simulations.