A novel collaborative position and orientation control scheme (CPOCS) for dual-arm robots is proposed, which is capable of controlling the end-effectors' positions with high precision while preserving their orientations unchanged to some practical tasks (e.g., box handling). To solve the proposed CPOCS in real time while considering key factors such as unknown bounded noise and strict time response constraints in practical engineering environments, this article proposes a novel precisely predefined-time convergent barrier recurrent neural network (PCB-RNN) based on a newly developed piecewise barrier evolution formula. Unlike existing RNNs, the proposed PCB-RNN, owing to its piecewise barrier evolution formula, can achieve precisely predefined-time convergence (PPTC) when addressing the proposed CPOCS under unknown bounded noise conditions. Comprehensive theoretical analysis rigorously proves the PPTC ability of the PCB-RNN under both noise-free and unknown bounded noise conditions. Furthermore, extensive simulation and physical experiments on dual-arm robots validate the effectiveness of the proposed CPOCS and demonstrate the advanced PPTC capability of the proposed PCB-RNN under unknown bounded noises.

Dynamic events, such as machine fault and rush order insertion, are fairly common in the job shop scheduling, which may lead to significant delay in order delivery and low production efficiency. Under such circumstance, it is urgent to consider more perspectives in the scheduling, such as delay time and equipment load rate. In this article, a dynamic many-objective flexible job shop scheduling problem (DMaFJSP) is founded to simultaneously optimize the completion time, delay time, total equipment load and energy consumption. Canonical many-objective optimization algorithms are seeing difficulties in maintaining population diversity and enduring poor adaptability in dynamic scheduling problems. The paper proposes a two-stage individual feedback non-dominated sorting genetic algorithm-III (TSIF-NSGA-III), where a new population diversity strategy and an individual feedback strategy are added to expand the global search faculty and stronger dynamic adaptability. Numerical study in many-objective problem and dynamic many-objective problem are conducted. The final results illustrate that the proposed algorithm can with effect dispose of the DMaFJSP.Note to Practitioners—This paper was motivated by the flexible job shop scheduling problem (FJSP) in practical dynamic situations. In the actual production procedure, however, FJSP is a more challenging issue. Not only operation sequencing and machine allocation matters, but also uncertain factors in the environment, such as machine fault, rush order insertion, etc., are important. In addition, the majority of current researchers formulate the FJSP simply focusing on maximum completion time. However, low carbon and high efficient manufacturing calls for more objectives. In this paper, two dynamic incidents, machine stoppage and rush order insertion, are considered. In addition, the model of DMaFJSP is established with many objectives such as total energy consumption, completion time, equipment load and delay time. To resolve foregoing problems, this article proposes a TSIF-NSGA-III algorithm, which adopts a diversity generation strategy and an individual feedback strategy to strengthen the search ability and dynamic adaptability of this algorithm. Preliminary simulation outcomes illuminate that this algorithm has certain advantages. In addition, the algorithm can also be applied to other multi-objective workshop scheduling problems, such as mixed flow workshop, distributed workshop, etc.

The performance of image-based visual servoing (IBVS) in dynamic, high-speed, and high-precision applications is a major issue in sustainable and smart manufacturing systems. The proposed solution addresses the need for systematic optimization of control laws and constraint treatments in IBVS processes. Limited exploration of this topic is evident in the literature. Central to our approach is a smart fuzzy control-based scheme optimized for the sustainable and intelligent operation of robotic arms in manufacturing environments. The scheme incorporates a Mamdani fuzzy inference method for the adaptive adjustment of servoing gain, improving convergence and aligning with smart manufacturing principles. This method ensures precision and responsiveness, which are essential for high-speed and high-precision tasks. We address field-of-view constraints through an innovative online generation method of virtual features with a variable radius in the image space. The effectiveness of this approach, which synergizes sustainable manufacturing with smart, technology-driven solutions, is demonstrated through various comparative experiments. The experimental results show that the number of convergence iterations and the average initial velocity of the proposed method are reduced to 59% and 12%, respectively, of those of the existing methods on average; the optimization of the convergence efficiency and the continuity of the initial speed are obvious. Additionally, the maximum value of the vertical coordinate of the image is 1011 pixel, and has the best security performance.

The technology of visual servoing, with the digital twin as its driving force, holds great promise and advantages for enhancing the flexibility and efficiency of smart manufacturing assembly and dispensing applications. The effective deployment of visual servoing is contingent upon the robust and accurate estimation of the vision‐motion correlation. Network‐based methodologies are frequently employed in visual servoing to approximate the mapping between 2D image feature errors and 3D velocities, offering promising avenues for improving the accuracy and reliability of visual servoing systems. These developments have the potential to fully leverage the capabilities of digital twin technology in the realm of smart manufacturing. However, obtaining sufficient training data for these methods is challenging, and thus improving model generalization to reduce data requirements is imperative. To address this issue, we offer a learning‐based approach for estimating Jacobian matrices of visual servoing that organically combines an extreme learning machine (ELM) and a differential evolutionary algorithm (DE). In the first stage, the pseudoinverse of the image Jacobian matrix is approximated using the ELM, which solves the problems associated with traditional visual servoing and is resistant to outside influences such as image noise and mistakes in camera calibration. In the second stage, differential evolution is utilized to select input weights and hidden layer bias and to determine ELM’s output weights. Experimental results conducted on a digital twin operating platform for 4‐DOF robot with an eye‐in‐hand configuration demonstrate better performance than classical visual servoing and traditional ELM‐based visual servoing in various cases.

Players-based emotion recognition can help the understanding game players’ emotional states, contributing to the improvement of the game's quality and value. This article develops a hybrid neural network learning framework called convolutional smooth feedback fuzzy network (CSFFN) to detect a player's emotional states in real-time during a gaming process based on electroencephalogram (EEG) signals. Specifically, CSFFN rationally combines a convolutional neural network (CNN), a fuzzy neural network (FNN), and a recurrent neural network (RNN). CNN not only captures spatial characteristics between EEG signals from different channels but also eliminates noise from EEG signals, improving the accuracy and anti-noise performance in game emotion recognition. FNN extracts the membership degree of a player's different emotional states, further improving the emotion recognition accuracy. Since a player's current emotional state is influenced by the previous emotional states during the game process, RNN is employed to capture the temporal characteristics of EEG signals, better improving the emotion recognition accuracy. Experimental results show that CSFFN has higher recognition accuracy and noise resistance in identifying four emotional states (happiness, sadness, superiority, and anger) compared to support vector machine (SVM) with different kernels, linear discrimination analysis (LDA), AlexNet, and VGG16 methods.

Technological advances in the Internet of Things (IoTs) and the evolution of manufacturing models applied in industrial environments increase the use of concepts, such as plug-and-play in intelligent manufacturing systems for customized products. However, plug-and-play manufacturing modes are subjected to dynamic changes in both products and resources during the runtime phase. Hence, traditional model-based formal verification methods cannot guarantee system dependability before and after the reconfiguration. In addition, customized manufacturing is a product-driven process, which requires product- and equipment-based modeling and analysis to be considered during the product life-cycle management. In this article, a model-data-driven (DD) framework for the formal analysis of production resources is proposed. An IoT-based DD technology is combined with a model-driven approach to ensure dependability verification of formal models in the operational production line phase through real-time resource status feedback. The proposed method is verified experimentally, using a smart factory experimental platform. The results show that the proposed method reduces the time-cost of the formal modeling phase by at least 75%.

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.

Most existing teleoperation technologies cannot guide robots to complete tasks quickly and accurately in unstructured environments. Therefore, this article proposes an intelligent interaction framework for teleoperation based on human-machine cooperation. The framework is divided into three layers: 1) perception, 2) decision-making, and 3) execution layers. In the perception layer, machines require high-precision environmental information, and human interaction requires rapid feedback on environmental changes. Therefore, a fast three-dimensional reconstruction method combining rough reconstruction and fine reconstruction is proposed. In the decision-making layer, a bare-hand interaction method based on natural interaction and an alignment assistance method based on constraint recognition are proposed. The combination of these two methods realizes the coordinated control of robot movement by humans and computers. In the execution layer, a vision-based error compensation method for assistance is proposed to decrease measurement errors and delays and reduce differences between virtual and real scenes. Finally, experimental results obtained by 15 nonprofessional volunteers show that the proposed method is efficient and user friendly.

In edge computing, heterogeneous automation tasks, such as networked motion control and reconfiguration management, have diverse weights on determinism and flexibility. Such heterogeneities escalate the demands on the runtime environments (RTEs) of programmable logic controllers to provide the best-suited rather than one-size-fits-all execution policies for reconfigurable programs. This article presents the proposal and implementation of a novel IEC 61499-based RTE capable of offering hybrid synchronous and asynchronous execution models for function block (FB) based programs. We also construct a runtime reconfigurable FB-type repository by realizing a just-in-time FB-type definitions compiler in the RTE. Finally, we evaluate and compare the proposed RTE and state-of-the-art works by running a set of synthetic benchmark programs and a realistic networked motion control kernel and application. Experiment results from the benchmark programs have shown a maximal 40% faster execution speed in our RTE. Furthermore, our RTE can ensure fewer jitters during distributed motion control with a 1-ms cycle time on a realistic motion control testbed.

The traditional production paradigm of large batch production does not offer flexibility toward satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multivariety and small-batch customized production modes. For this, artificial intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are: self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This article focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, that is, machine learning, multiagent systems, Internet of Things, big data, and cloud-edge computing, are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

Joint angle error of robotic arm has great impacts on the accuracy of the end-effector, which is critical in industrial applications. Therefore, in this article, an online cognitive joint angle error compensation method based on incremental learning is proposed to reduce joint angle error. The proposed method consists of a joint angle error solver and a compensation module, which ensure that the robot can obtain effective joint angle compensation in various situations. The joint angle error solver is used to solve joint angle error online. It uses the redundant constraint method for multilink position measurement so as to calculate the position error of the robot accurately later. The compensation module uses the self-feedback incremental fuzzy neural network (SFIFN) to predict and update the compensation in real time. SFIFN is a variant of the fuzzy neural network (FNN), which uses long short-term memory to introduce a feedback mechanism based on FNN. The incremental learning capability of SFIFN reduces the time for solving error and makes the module runs in real time. Specifically, two inertial measurement units mounted at the ends of links are used to measure pose changes of the ends of corresponding links. Both the simulated and the real experiments show that the proposed method yields good compensations to joint angle error and its potentials for smart manipulation.

The emergence of fog computing has improved the performance and efficiency of intelligent manufacturing. In a fog computing platform, fog nodes can provide the distributed and proximity resources for the tasks related to intelligent manufacturing. Due to the heterogeneous characteristics of the fog nodes and the space-time characteristics of intelligent manufacturing, the effective deployment of the fog nodes is a precondition for the fog computing implementation and it is also a key to provide high-performance services with the fog computing platform. However, traditional deployment strategies cannot satisfy the performance requirements of intelligent manufacturing systems. In this article, the problem of the fog nodes deployment is studied and a fog nodes deployment strategy based on the space-time characteristics (TSBP) is proposed. A fog nodes deployment system model is set up and the objective function of the fog nodes deployment is built up, in which the optimization goal is to minimize the computing response time and realize the load balancing of the fog nodes. In addition, the discrete differential evolution algorithm is applied to search the optimal fog nodes deployment solution. Finally, the effectiveness of the proposed TSBP strategy is verified using a candy packaging intelligent production line prototype platform.

Driving fatigue can be detected by measuring drivers' heart rate with a wearable device or extracting their facial features with an RGB camera. However, a wearable device causes inconvenience and discomfort to the driver, and an RGB camera's detection accuracy may be affected by light, glasses, and head orientation. Furthermore, most existing methods ignored the temporal information of fatigue features and the relationship between the features, lowering recognition accuracy. Additionally, some existing fatigue detection methods focused on dealing with fatigue features with a temporal slice, ignoring temporal variations in the features. To address these problems, a single RGB-D camera is first used to extract three fatigue features: heart rate, eye openness level, and mouth openness level. More importantly, this paper proposes a novel multimodal fusion recurrent neural network (MFRNN), integrating the three features to improve the accuracy of driver fatigue detection. Specifically, a recurrent neural network (RNN) layer is applied in the MFRNN to obtain the temporal information of the features. Since the heart rate feature is a physiological signal extracted indirectly, it contains more noise and is fuzzier than the other features. To deal with the fuzziness and noise, we combine fuzzy reasoning with RNN to extract the temporal information of the heart rate. To identify the relationship between the features, we develop a new relationship layer containing a two-level RNN, for which the input is the temporal information of the features. Both the simulation and field experiment results show that the proposed method provides better performance than similar methods.

The development of Industry 4.0 has provided the possibility to meet frequent changes in product type and batches, a sharp decline in the delivery cycle, constraints of quality cost, and other relevant parameters of customized production mode. Intelligent manufacturing, as a core of Industry 4.0, represents a deep integration of new IT technologies, such as the industrial Internet of Things and service-oriented architecture, and manufacturing process. To realize intelligent manufacturing, this article introduces a cloud-assisted and edge-decision-making manufacturing architecture that contains a cloud and production edges. An intelligent production edge is designed to provide the traditional devices the abilities of data access and self-decision making. Besides, the proposed architecture is modeled as a multiagent system with the edge intelligence support, describing the agent-based reconfiguration mechanism from the three aspects, namely, agent interaction, agent behavior, and negotiation mechanism. The experimental results show that the reconfigurable method based on the proposed architecture can be used in the mixed-flow production scenario based on random orders, to improve the adaptability and robustness.

In the context of Industry 4.0, extensive deployment and application of advanced manufacturing equipment and various sensors is leading to a growing demand for data exchange between different devices. In smart factories, network transmission has multiprotocol features of wired/wireless communication, and different data flows have different real-time requirements. In this article, a heterogeneous network architecture based on software-defined network is proposed for realizing cross-network flexible forwarding of multisource manufacturing data and optimized utilization of network resources. Subsequently, the mechanism of cross-network fusion and scheduling (CNFS) is analyzed from the perspective of high dynamic characteristics and different delay requirements of data flows. Based on this analysis, a route-aware data flow dynamic reconstruction algorithm is proposed. The proposed algorithm improves the efficiency of manufacturing data cross-network fusion, especially for multivariety and small-batch intelligent manufacturing systems. Furthermore, for meeting the bandwidth requirements of different delay flows, a delay-sensitive network bandwidth scheduling algorithm is proposed. Finally, the effectiveness of the proposed CNFS mechanism is verified using a candy packaging intelligent production line prototype platform.

Through the Industrial Internet of Things (IIoT), a smart factory has entered the booming period. However, as the number of nodes and network size become larger, the traditional IIoT architecture can no longer provide effective support for such enormous system. Therefore, we introduce the Blockchain architecture, which is an emerging scheme for constructing the distributed networks, to reshape the traditional IIoT architecture. First, the major problems of the traditional IIoT architecture are analyzed, and the existing improvements are summarized. Second, we introduce a security and privacy model to help design the Blockchain-based architecture. On this basis, we decompose and reorganize the original IIoT architecture to form a new multicenter partially decentralized architecture. Then, we introduce some relative security technologies to improve and optimize the new architecture. After that we design the data interaction process and the algorithms of the architecture. Finally, we use an automatic production platform to discuss the specific implementation. The experimental results show that the proposed architecture provides better security and privacy protection than the traditional architecture. Thus, the proposed architecture represents a significant improvement of the original architecture, which provides a new direction for the IIoT development.

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.

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.

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.

To accommodate the trend toward mass customization launched by intelligent manufacturing, the paper proposes the adoption of model-integrated computing (MIC) paradigm in the motion control system development process for enhancing flexibility and robustness. Hierarchical structural and behavioral diversities in motion control system are considered during the implementation of MIC paradigm. For design-phase implementation, a motion-control-domain-specific modeling language is developed, and formal semantics are integrated. With regard to execution-phase implementation, a real-time runtime framework compliant with the IEC 61499 standard is proposed. Extensions of function block chain and priority-based event propagation are proposed. Dynamically extendable FB types library for motion control domain is constructed. A prototype three-axis motion control system is modeled using the proposed modelling language and is then deployed to the implemented framework to prove the feasibility of the adoption of the MIC paradigm in motion control domain.

The multidisciplinary nature and time criticality of computing in Cyber-Physical Robot Systems (CPRS) makes it significantly different from traditional computer systems. This article attempts to create a usage-specific language called Cyber-Physical Robot Language (CPRL), which supports the CPRS design and implementation in an integrative and swift way. Multiview description and integration strategies as well as formal execution semantics for usage-specific simulation and verification are outlined. A graphic unified environment for CPRS modeling is supplied, in which several tools are integrated. A 6-DOF distributed robot system development in the environment is presented. The approach is an attempt to support CPRS design in an effective way, at the same time guaranteeing the system function and performance requirements.

Recently, cyber-physical systems (CPSs) have emerged as a cutting edge technology for next-generation industrial applications, and are undergoing rapid development and inspiring numerous application domains. In this article, we propose a novel CPS application for energy management framework (EMF) toward autonomous electric vehicle (AEV) in smart grid. We first give a brief overview of related technologies, including open research issues of CPS, enabling wireless communication technologies for CPS, smart grid, AEV and its path planning, energy-efficient design for AEV, etc. Then we design EMF from the following aspects, such as AEV with wireless sensor networks (WSNs) navigation, smart grid communication architecture for EMF and AEV charging station. The proposed EMF is able to collect the real-time power consumption status and demand from AEV and charging stations. We also address EMF to overcome some issues, such as real-time traffic information. Subsequently, the energy-efficient design schemes for AEV are proposed and formulated from the point of view of path planning and event-based control technique. Finally, we outline the issues and challenges for EMF.