As modern agriculture progresses, the swift deployment of accurate maps becomes essential for the autonomous navigation and operation of orchard robots. Traditional mapping techniques often fall short in addressing the challenges posed by orchards, which are characterized by unstructured, dynamically changing environments with complex spatial and temporal dynamics due to seasonal and continuous operations. This paper proposes a new approach to orchard map construction that merges topological maps with semantic SLAM. This integration enables the creation, optimization, and rapid deployment of maps that are not only lightweight and robust but also precise. To evaluate the effectiveness of our method, we performed navigation tests in orchard environments using the newly developed maps. The experimental outcomes demonstrated a significant reduction in CPU usage, with maximum and average reductions of 7.6% and 4.5%, respectively. This approach not only enhances navigation efficiency but also facilitates quicker map deployment, effectively freeing computational resources for other critical tasks.

Motor fault diagnosis is a fundamental aspect of ensuring the reliability of industrial equipment. However, industrial scenarios exhibit an inherent data scarcity problem, which imposes significant restrictions on the practical application of traditional deep learning-based intelligent fault diagnosis (IFD) methods. Typically, only a small volume of labeled data along with limited informative unlabeled data are available from industrial motors. Effectively utilizing informative unlabeled samples in the context of few-shot fault diagnosis poses a substantial challenge. In this article, a prototype refinement method for semi-supervised few-shot fault diagnosis based on deep reinforcement learning (DRL) is proposed. First, we propose to formalize a Markov decision process (MDP) of an iterative semi-supervised meta-learning strategy involving the selection of informative unlabeled samples and the refinement of category prototypes. Subsequently, we develop a mirror prototypical network (ProtoNet) structure for interaction with a DRL agent, which learns to adaptively select valuable samples to supervise the diagnosis process. Moreover, a state space involving feature embedding and category information is designed, and a comprehensive reward taking into account selection confidence, effectiveness, and representative is proposed. Extensive experiments on several motor experimental datasets verify the method's effectiveness in few-shot diagnosis of unseen faults and new working conditions.

In a GPS-hindered indoor environment, precise positioning is a challenge for mobile robots that perform accurate operations. For instance, during ridge-raising operations in plant factories, robots require positioning accuracy upto 5 cm, which is a pending issue in industry. To address this problem, we propose a ultraprecise simultaneous localization and mapping (SLAM) positioning method, namely, preciseSLAM, which tightly couples LiDAR, inertial measurement unit (IMU), and ultrasonic sensors to achieve ultraprecision, robustness, and real-time positioning performance. First, the preciseSLAM framework is established by fusing multimodal data from LiDAR, IMU, and ultrasonic sensors. Second, to effectively reduce naive SLAM positioning drift after long-term operation, a confidence zone optimization method is proposed for ultrasonic global positioning information. Finally, a factor graph optimization algorithm is developed to tightly couple four preciseSLAM factors, i.e., LiDAR odometry, IMU odometry, loop closure, and the specific ultrasonic global positioning factor. The proposed method effectively addresses the challenges in plant factory environments and can provide reliable and accurate positioning for intelligent robots. Experimental results demonstrate that preciseSLAM achieves a remarkable positioning accuracy of 5.4 cm in the indoor environment inside a plant factory. Compared with that of existing methods, preciseSLAM exhibits ultraprecision, superior robustness, and real-time performance. preciseSLAM provides a general-purpose positioning and navigation solution for robots in large-scale plant factories, which is among the very few methods that can deal with precise indoor positioning.

Many signals in the real world are nonlinear and complex, and signal time-frequency analysis has been widely used in many fields. It remains a challenging task to accurately decompose amplitude modulation-frequency modulation (AM-FM) signals with complex variation laws. The existing methods still have room to improve the decomposition accuracy of complex AM-FM signals, such as signals with crossing instantaneous frequency (IF) or close IFs. This article presents a novel sparsity-assisted variational nonlinear component decomposition (SVNCD) for analyzing nonstationary multicomponent signal with complex variation laws. To better restrict the bandwidth of demodulated signal and accurately estimate IF, SVNCD establishes a new sparse optimization model with clear mathematical meaning for the demodulated signal of the original signal, considering the smoothness and Fourier spectrum constraints. Moreover, SVNCD establishes a sparse constraint optimization model on the IF of the signal and its increment, which is capable of effectively extracting the IF variation law of complex signals and addressing the mode aliasing problem. Besides, we build a unified framework for extracting the initial IFs of complex signals with uncrossing or crossing frequency trajectories. The decomposition experiments of various simulated and experimental complicated signals are carried out for verification. The results verify that SVNCD achieves better decomposition accuracy for complex nonstationary multicomponent signals with uncrossing or crossing frequency trajectories than existing signal decomposition and time-frequency transform methods. Moreover, SVNCD could accurately estimate the IF of the original signal and reconstruct the subsignals.

A large amount of data acquisition and annotation work is required to train a supervised machine learning model for open scenes. However, traditional manual approaches are inefficient. Here, a method is proposed for on-site image acquisition and semiautomatic annotation based on eye-tracking. This method uses the recognition capabilities and computational advantages of humans and machines to improve annotation efficiency, overcoming the bottleneck of AI-based approaches to the natural scenery understanding of field robots. The proposed method contains three advancements. First, we designed a head-mounted display with a built-in pose measurement module to achieve first-person teleoperation data acquisition, where a pseudoframe interpolation algorithm is designed to overcome the latency problem in immersive remote data transmission and to achieve efficient field data acquisition. Second, we propose an adaptive superpixel segmentation algorithm to reduce human–machine interactions based on eye-tracking. Third, since traditionally, the annotation process cannot provide feedback to the acquisition process and results in a low conversion rate. We proposed a new conversion rate index denoting the rate of transforming collected data into valid data to quantify the acquisition quality in real time. While achieving an annotation quality of 0.964 per the Dice index, which is approximately equal to that of the manual method, the proposed method improves the annotation efficiency by more than 3 times. Finally, the agricultural field experiments containing a real-life scene of robotic tomato-picking verified that the proposed method based on human–computer interaction can make full use of human perception and recognition intelligence.

Recent technological advances and manufacturing paradigm evolutions in industrial settings will dramatically increase the complexity of automation control systems. Traditional solutions to the software development of low-level control kernels (e.g., numerical control kernel, motion control kernel, and real-time communication tasks) are unable to cope effectively with such complexity due to an inadequate level of abstraction and challenges for dependability. This article presents a formal semantics integrated model-driven design approach as a holistic solution. A domain-specific modeling language (DSML) is specified based on the adaption of IEC 61 499 architecture, along with the extensions of task model, task-to-resource allocation, and nonfunctional specification. Both formal structural and behavioral semantics of the proposed DSML are then explicitly defined. Design-time formal verification is also achieved by automated model transformations. A metaprogrammable environment is adopted to facilitate flexible modeling, verification, and code generation. A case study is demonstrated on implementing a prototype computer numerical control (CNC) system using the proposed solution.Note to Practitioners—The low-level automation control system in the modern manufacturing scenarios require more agility while respecting strict timing constraints. Handling such complexity with manual coding is getting harder and less efficient. The DSML and the supporting development environment presented in this article aim to enhance the level of automation, flexibility, and dependability of the whole design process. For the proposed DSML, its syntax is formalized and defined as metamodels, while the semantics is integrated through model annotation and transformation. These definitions are implemented as external rules for a metaprogrammable environment to establish our proposed development tool. The finding and insight from this article can enhance efficiency and dependability during the development of common control kernels, such as CNC kernel and motion controller.

Machinery remaining useful life (RUL) prediction is an important task in condition-based maintenance. Data-driven methods have been widely studied and applied, however, almost all the researches learn degradation trends regardless of different fault conditions, which can lead to different degradation patterns. This article proposes a novel fault information assisted RUL prediction method based on a convolutional long short-term memory (LSTM) ensemble network, where fault conditions are obtained via fault knowledge transfer. Divergence minimization and domain adversarial adaptation are combined to transfer fault knowledge from a fault dataset to the run-to-failure data in a weakly supervised manner. With the predicted fault information, the RUL prediction network can learn various degradation patterns under different faults separately using a structure of multiple LSTMs. Then an ensemble strategy based onsoft fault conditionsis designed to get final RUL prediction results. Experiment on bearing datasets verifies the effectiveness of our proposed method.

Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low-level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method.

The aim of this paper is to realize multi-objective optimal control of a switched reluctance machine considering three criteria, namely, torque ripple, vibration (radial force)and RMS current. A harmonic model is used during optimization, which represents current profile, radial force factor and torque factor in Fourier series. Multi-physics model is built based on harmonic model, in which the three criteria are linear functions of harmonic amplitude contained in phase currents. A collaborative optimization is proposed to obtain current profile considering all three criteria. Weight coefficients are assigned to three criteria to guide trade-off during optimization. Current profiling is implemented under four modes, which put emphasis on different criteria. The torque ripple, radial force and RMS current under four modes are compared through simulation and experiments. It is demonstrated that the three criteria are effectively adjusted through regulating weight coefficients, the proposed method gives optimal current profiles with designated compromise between multiple performance criteria and avoids some criteria being severely deteriorated while others are optimized.

In recent years, smart factory in the context of Industry 4.0 and industrial Internet of Things (IIoT) has become a hot topic for both academia and industry. In IIoT system, there is an increasing requirement for exchange of data with different delay flows among different smart devices. However, there are few studies on this topic. To overcome the limitations of traditional methods and address the problem, we seriously consider the incorporation of global centralized software defined network (SDN) and edge computing (EC) in IIoT with EC. We propose the adaptive transmission architecture with SDN and EC for IIoT. Then, according to data streams with different latency constrains, the requirements can be divided into two groups: 1) ordinary and 2) emergent stream. In the low-deadline situation, a coarse-grained transmission path algorithm provided by finding all paths that meet the time constrains in hierarchical Internet of Things (IoT). After that, by employing the path difference degree (PDD), an optimum routing path is selected considering the aggregation of time deadline, traffic load balances, and energy consumption. In the high-deadline situation, if the coarse-grained strategy is beyond the situation, a fine-grained scheme is adopted to establish an effective transmission path by an adaptive power method for getting low latency. Finally, the performance of proposed strategy is evaluated by simulation. The results demonstrate that the proposed scheme outperforms the related methods in terms of average time delay, goodput, throughput, PDD, and download time. Thus, the proposed method provides better solution for IIoT data transmission.

In the context of Industry 4.0, industrial robotics such as automated guided vehicles have drawn increased attention due to their automation capabilities and low cost. With the support of cognitive technologies for industrial Internet of Things (IoT), production processes can be significantly optimized and more intelligent manufacturing can be implemented for smart factories. In this paper, for advanced material handling, a cognitive industrial entity called context-aware cloud robotics (CACR) are introduced and analyzed. Compared with the one-time on-demand delivery, CACR is characterized by two features: (1) context-aware services and (2) effective load balancing. First, the system architecture, advantages, challenges, and applications for CACR are introduced. Then, fundamental functions for material handling are articulated, namely, decisionmaking mechanisms and cloud-enabled simultaneous localization and mapping. Finally, a CACR case study is performed to highlight its energy-efficient and cost-saving material handling capabilities. Simulations indicate the superiority of cognitive industrial IoT and show that using CACR for material handling can significantly improve energy efficiency and save cost.

We present a method of discriminant diffusion maps analysis (DDMA) for evaluating tool wear during milling processes. As a dimensionality reduction technique, the DDMA method is used to fuse and reduce the original features extracted from both the time and frequency domains, by preserving the diffusion distances within the intrinsic feature space and coupling the features to a discriminant kernel to refine the information from the high-dimensional feature space. The proposed DDMA method consists of three main steps: (1) signal processing and feature extraction; (2) intrinsic dimensionality estimation; (3) feature fusion implementation through feature space mapping with diffusion distance preservation. DDMA has been applied to current signals measured from the spindle in a machine center during a milling experiment to evaluate the tool wear status. Compared with the popular principle component analysis method, DDMA can better preserve the useful intrinsic information related to tool wear status. Thus, two important aspects are highlighted in this study: the benefits of the significantly lower dimension of the intrinsic features that are sensitive to tool wear, and the convenient availability of current signals in most industrial machine centers.

Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.

Owing to the semantic ambiguities, it has hindered the promotion of IEC 61499 in the field of industrial automation. In order to solve the thorny problem, this paper proposes an implementation scheme for performing formal modeling and simulation verification of semantics of functional block networks. Based on the synchrony hypothesis, the formal execution model is defined according to the fixed point semantics assuming that the behavior of a component functional block is monotonic. Subsequently, through specifying the evaluation of function blocks (FBs) as a process of solving the least-fixed point problem and transforming the network topology into a directed graph, a connectivity attenuation-based algorithm is put forward to ascertain the optimal scheduling policy of FBs with the minimum overhead. Finally, by conducting the experiment for an industrial application, the feasibility and validity of the presented implementation scheme is proved.

Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.