Search Results (290)

In emergency scenarios, we focus on studying how to manipulate legged robot dogs equipped with robotic arms to move and operate in a small space, known as legged emergency manipulation. Although the legs of the robotic dog are mainly used for movement, we found that implementing a whole-body control strategy can enhance its operational capabilities. This means that the robotic dog’s legs and mechanical arms can be synchronously controlled, thus expanding its working range and mobility, allowing it to flexibly enter and exit small spaces. To this end, we propose a framework that can utilize visual information to provide feedback for whole-body control. Our method combines low-level and high-level strategies: the low-level strategy utilizes all degrees of freedom to accurately track the body movement speed of the robotic dog and the position of the end effector of the robotic arm; the advanced strategy is based on visual input, intelligently planning the optimal moving speed and end effector position. At the same time, considering the uncertainty of visual guidance, we integrate fully supervised learning into the advanced strategy to construct a teacher network and use it as a benchmark network for training the student network. We have rigorously trained these two levels of strategies in a simulated environment, and through a series of extensive simulation validations, we have demonstrated that our method has significant improvements over baseline methods in moving various objects in a small space, facing different configurations and different target objects. Full article

The shift to sustainable energy requires a thorough understanding of the elements affecting policy adoption, especially regarding political and economic dynamics. Current approaches, such as the technology acceptance model (TAM), theory of planned behavior (TPB), and unified theory of acceptance and use of technology (UTAUT), mainly emphasize individual behavioral aspects, often neglecting macro-level implications. This research uses the hybrid model for energy policy adoption (HMEPA) to bridge this gap, including economic and political factors with behavioral theories to evaluate energy policy acceptability. We propose that social impact, attitudes toward the policy, and financial and political considerations substantially affect stakeholders’ acceptance intentions. We gathered 421 valid answers from people in Taiwan using a questionnaire survey and analyzed the data using structural equation modeling (SEM). The findings demonstrate that whereas effort expectation and enabling circumstances have little impact, social influence and attitude are the most significant determinants of policy adoption intention. Moreover, political variables influence attitudes and social dynamics, while economic policy impacts performance expectations, perceived behavioral control, and enabling circumstances. These results underscore the need to synchronize policy plans with political and economic realities. Policymakers may use these findings to formulate stakeholder-oriented policies that promote sustainable energy transitions. Full article

In this paper, a novel fractional-order chaotic system equipped with symmetric attractors was proposed for the full-coverage path-planning problem of mobile robots, especially in application scenarios where path privacy needs to be protected. By coupling this system with a kinematic model of a mobile robot, a novel path-planning algorithm was designed to realize encrypted full-coverage path planning. A predefined time-synchronization control strategy effectively resolved inconsistencies in the path caused by initial position, time delay, and uncertain disturbances. Numerical simulation results demonstrated that the proposed path-planning method, based on the novel chaotic system, significantly improved coverage and randomness, compared to existing studies. Moreover, it maintained accuracy and stability in path planning, even in the presence of time delays and uncertain disturbances. Full article

In the era of precision medicine, mounting evidence suggests that the time of therapy administration, or chronotherapy, has a great impact on treatment outcomes. Chronotherapy involves planning treatment timing by considering circadian rhythms, which are 24 h oscillations in behavior and physiology driven by synchronized molecular clocks throughout the body. The value of chronotherapy in cancer treatment is currently under investigation, notably in the effects of treatment timing on efficacy and side effects. Immune checkpoint inhibitor (ICI) therapy is a promising cancer treatment. However, many patients still experience disease progression or need to stop the therapy early due to side effects. There is accumulating evidence that the time of day at which ICI therapy is administered can have a substantial effect on ICI efficacy. Thus, it is important to investigate the intersections of circadian rhythms, chronotherapy, and ICI efficacy. In this review, we provide a brief overview of circadian rhythms in the context of immunity and cancer. Additionally, we outline current applications of chronotherapy for cancer treatment. We synthesize the 29 studies conducted to date that examine the impact of time-of-day administration on the efficacy of ICI therapy, its associated side effects, and sex differences in both efficacy and side effects. We also discuss potential mechanisms underlying these observed results. Finally, we highlight the challenges in this area and future directions for research, including the potential for a chronotherapeutic personalized medicine approach that tailors the time of ICI administration to individual patients’ circadian rhythms. Full article

In the field of multi-robot cooperative localization and task planning, traditional filtering algorithms encounter synchronization and consistency issues during multi-source data fusion. These challenges result in cumulative localization errors and inefficient information sharing, which limits the system’s collaborative capabilities and control accuracy. To overcome these limitations, a distributed cooperative navigation strategy is introduced. Initially, a Distributed Adaptive Extended Kalman Filter (DAEKF) is implemented, which adaptively adjusts the noise covariance matrix to effectively manage nonlinearities and multi-source noise conditions. Subsequently, a Distributed Model Predictive Control (DMPC) framework is introduced. This framework predicts and optimizes each robot’s kinematic model, thereby improving the system’s collaborative operations and dynamic decision-making capabilities. Finally, the efficacy of this strategy is confirmed through detailed simulations and robotic experiments. The simulation results for cooperative localization demonstrate that DAEKF outperforms Kalman Filter (KF) and Extended Kalman Filter (EKF) in terms of localization accuracy. In the straight-line path-tracking experiments, DAEKF effectively reduced both lateral and heading errors for both robots. For Robot 1, DAEKF reduced the lateral error Root Mean Squared Error (RMSE) by 68.87%, 27.80%, and 25.76%, compared to No Filtering, KF, and EKF. In heading error, DAEKF reduced the RMSE by 52.29%, 41.89%, and 36.47%. For Robot 2, DAEKF reduced the lateral error RMSE by 51.30%, 22.88%, and 11.60%, compared to No Filtering, KF, and EKF. In heading error, DAEKF reduced the RMSE by 39.55%, 37.15%, and 26.00%. In the curved path-tracking experiments, both robots demonstrated high trajectory conformity while traveling along a predefined path combining straight-line and circular arc segments, with lateral errors in the straight-line segments all below 0.05 m. The strategy proposed in this study significantly enhanced the precision and stability of multi-robot collaborative navigation, demonstrating strong practicality and scalability. Full article

Relocatable modular buildings (RMBs) offer significant advantages, including flexibility, mobility, and scalability, making them ideal for temporary or rapidly changing scenarios. However, as the scale and quantity of RMB modules increase, their allocation across projects poses complex logistical challenges. Inefficiencies in traditional manual allocation methods, such as suboptimal module selection, increased transportation costs, and project delays, underscore the need for innovative solutions. This study develops a Digital Twin (DT)-based decision support system to optimize the allocation and management of RMB modules. The proposed framework integrates Building Information Modeling (BIM), Internet of Things (IoT), and Geographic Information Systems (GISs), enabling the real-time synchronization of physical assets with their digital counterparts. The DT framework incorporates real-time data acquisition, dynamic module condition assessments, and an algorithm-driven allocation process to streamline resource utilization and logistics planning. The system is validated through a case study of South Korea’s first relocatable modular school system project, demonstrating its capability to optimize module allocation, reduce costs, and enhance lifecycle management. This study advances RMB management by offering a practical, data-driven approach, empowering facility managers to leverage real-time data for preventive maintenance, asset optimization, and sustainable resource utilization. Full article

How to consider the differentiated inertia levels of each node during the retirement of coal-fired power units to ensure system frequency security is a current research challenge. This paper proposes a source-grid collaborative planning method that accounts for the differentiated inertia levels of network nodes during the coal-fired power unit retirement. First, a frequency response model based on a multi-machine equivalence approach and a differentiated inertia level model based on a virtual synchronous machine transformation approach for each network node are established, and a node inertia constraint model can be obtained. Next, the characteristics of coal-fired power unit retirement are analyzed. Subsequently, a source-grid collaborative planning model that considers the differentiated inertia levels of network nodes during the retirement of coal-fired power units is proposed. Finally, the feasibility and effectiveness of the coal-fired unit retirement and source-grid planning model are validated by the IEEE 24-node case and a real-region case. The case study analysis indicates that compared with the conventional planning scheme, the planning scheme considering the node inertia level constraints has less retired thermal unit capacity, more installed capacity of new thermal units, and a uniform distribution of inertia levels. Full article

This paper deals with a “digital twin” (DT) approach for processing, reprocessing, and scrapping (P/R/S) technology running on a modular production system (MPS) assisted by a mobile cyber–physical robotic system (MCPRS). The main hardware architecture consists of four line-shaped workstations (WSs), a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM) and a mobile visual servoing system (MVSS) mounted on the end effector. The system architecture integrates a hierarchical control system where each of the four WSs, in the MPS, is controlled by a Programable Logic Controller (PLC), all connected via Profibus DP to a central PLC. In addition to the connection via Profibus of the four PLCs, related to the WSs, to the main PLC, there are also the connections of other devices to the local networks, LAN Profinet and LAN Ethernet. There are the connections to the Internet, Cloud and Virtual Private Network (VPN) via WAN Ethernet by open platform communication unified architecture (OPC-UA). The overall system follows a DT approach that enables task planning through augmented reality (AR) and uses virtual reality (VR) for visualization through Synchronized Hybrid Petri Net (SHPN) simulation. Timed Petri Nets (TPNs) are used to control the processes within the MPS’s workstations. Continuous Petri Nets (CPNs) handle the movement of the MCPRS. Task planning in AR enables users to interact with the system in real time using AR technology to visualize and plan tasks. SHPN in VR is a combination of TPNs and CPNs used in the virtual representation of the system to synchronize tasks between the MPS and MCPRS. The workpiece (WP) visits stations successively as it is moved along the line for processing. If the processed WP does not pass the quality test, it is taken from the last WS and is transported, by MCPRS, to the first WS where it will be considered for reprocessing or scrapping. Full article

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system’s potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy. Full article

The combination of human cognitive skills and dexterity with the endurance and repeatability of robots is a promising approach to modern assembly. However, efficiently allocating tasks and planning an assembly sequence between humans and robots is a manual, complex, and time-consuming activity. This work presents a framework named “Extract–Enrich–Assess–Plan–Review” that facilitates holistic planning of human–robot assembly processes. The framework automatically Extracts data from heterogeneous sources, Assesses the suitability of each assembly step to be performed by the human or robot, and Plans multiple assembly sequence plans (ASP) according to boundary conditions. Those sequences allow for a dynamic adaptation at runtime and incorporate different human–robot interaction modalities that are Synchronized, Cooperative, or Collaborative. An expert remains in the loop to Enrich the extracted data, and Review the results of the Assess and Plan steps with options to modify the process. To experimentally validate this framework, we compare the achieved degree of automation using three different CAD formats. We also demonstrate and analyze multiple assembly sequence plans that are generated by our system according to process time and the interaction modalities used. Full article

Remote sensing is heavily relied upon where eelgrass maps are needed for tracking trends, project siting and permitting, water quality assessments, and restoration planning. However, there is only a moderate degree of confidence in the accuracy of maps derived from remote sensing, thus risking inadequate resource protection. In this study, semi-synchronous drone, side-scan sonar, airplane, and satellite missions were conducted at five Massachusetts eelgrass meadows to assess each method’s edge-detection capability and mapping accuracy. To ground-truth the remote sensing surveys, SCUBA divers surveyed the meadow along transects perpendicular to shore to locate the last shoot (i.e., meadow’s edge) and sampled quadrat locations along the transect for percent cover, canopy height, and meadow patchiness. In addition, drop frame underwater camera surveys were conducted to assess the accuracy of each remote sensing survey. Eelgrass meadow delineations derived from each remote sensing method were compared to ground-truthing data to address the following study objectives: (1) determine if and how much eelgrass was missed during manual photointerpretation of the imagery from each remote sensing method, (2) assess map accuracy, as well as the effects of eelgrass percent cover, canopy height, and meadow patchiness on method performance, and (3) make management recommendations regarding the use of remote sensing data for eelgrass mapping. Results showed that all remote sensing methods were associated with the underestimation of eelgrass. At the shallow edge, mean edge detection error was lowest for drone imagery (11.2 m) and increased with decreasing image resolution, up to 38.5 m for satellite imagery. At the deep edge, mean edge detection error varied by survey method but ranged from 72 to 106 m. Maximum edge detection errors across all sites and depths for each survey method were 112.4 m, 121.4 m, 121.7 m, and 106.7 m for drone, sonar, airplane, and satellite data, respectively. The overall accuracy of eelgrass delineations across the survey methods ranged from 76–89% and corresponded with image resolution, where drones performed best, followed by sonar, airplanes, and satellites; however, there was a high degree of site variability. Accuracy at the shallow edge was greater than at the deep edge across all survey types except for satellite, where accuracy was the same at both depths. Accuracy was influenced by eelgrass percent cover, canopy height, and meadow patchiness. Low eelgrass density (i.e., 1–10% cover), patchy eelgrass (i.e., shoots or patches spaced > 5 m) and shorter canopy height (i.e., <22 cm) were associated with reduced accuracy across all methods; however, drones performed best across all scenarios. Management recommendations include applying regulatory buffers to eelgrass maps derived from remote sensing in order to protect meadow edge areas from human disturbances, the prioritization of using SCUBA and high-resolution platforms like drones and sonar for eelgrass mapping, and for existing mapping programs to allocate more resources to ground-truthing along meadow edges. Full article

Offshore wind is a promising renewable energy generation technology and is arousing great attention in regards to pursuing carbon neutrality targets. Accurately simulating offshore wind generation can help to better optimize its operation and planning. It is also a concern that mechanical resonance is a threat to the wind turbines’ lifespan. In this paper, the time-series simulation of offshore wind generation with consideration of resonance zone (RZ) control is investigated. The output model for multiple wind farms with different spatial correlations is proposed. Additionally, the capacity value (CV) of the joint wind farms is also evaluated through a reliability-based model. The case study illustrates the offshore wind power output simulation and CV results under different farm correlation scenarios and RZ control strategies. It is shown that strong spatial correlation brings great synchronicity in wind farms’ output and results in a lower CV. The RZ control in wind simulation is validated and proven to have a marginal impact on the total output when multiple wind farms are evaluated together. Full article

This paper examines the impact of Vehicle-to-Everything (V2X) communications on vehicle cooperation, focusing on increasing the robustness and feasibility of Cooperative, Connected, and Automated Vehicles (CCAVs). V2X communications enable CCAVs to obtain a holistic environmental perception, facilitating informed decision making regarding their trajectory. This technological innovation is essential to mitigate accidents resulting from inadequate or absent communication on the roads. As the importance of vehicle cooperation grows, the European Telecommunications Standards Institute (ETSI) has been standardizing messages and services for V2X communications, in order to improve the synchronization of CCAVs actions. In this context, this preliminary work explores the use of Maneuver Coordination Messages (MCMs), under standardization by ETSI, for cooperative path planning. This work presents a novel approach by implementing these messages as well as the associated Maneuver Coordination Service (MCS) with a Cooperative Driving System to process maneuver coordination. Additionally, a trajectory approach is introduced along with a message generation mechanism and a process to dynamically handle collisions. This was implemented in an Artery V2X simulation framework combining both network communications and SUMO traffic simulations. The obtained results demonstrate the effectiveness of using V2X communications to ensure the safety and efficiency of Cooperative Intelligent Transportation Systems (C-ITS). Full article

In low-inertia systems with a high penetration of renewable energy, the rotational kinetic energy and inertia constant are significant factors in determining frequency stability. The energy released owing to the frequency decrease during contingency represents a portion of the inertia that a synchronous machine possesses in the normal state. However, when securing inertia or planning additional resources to secure frequency stability, inertia in the normal state is analyzed as the standard rather than the amount of energy released during a fault. Therefore, in this paper, we define the actual energy emitted from a synchronous machine as Effective inertia. In order to evaluate Effective inertia in various operating conditions, we conducted a comprehensive review on approximately 24,627 cases from the years 2019, 2020, and 2021. As a result, in systems with low rotational kinetic energy, both low- and high-frequency nadirs were observed, indicating high uncertainty. However, Effective inertia presented a consistent trend regarding the energy release aligned with the minimum frequency. For instance, the rotational kinetic energy required to satisfy the frequency standard was 23 GWs, while the required Effective inertia was 858 MWs. We emphasize that securing inertia based on rotational kinetic energy includes additional imaginary energy that does not contribute to frequency, resulting in an energy requirement greater than that needed for Effective inertia. Therefore, in order to secure the frequency stability of the future system, the actual required energy amount based on Effective inertia will be presented and utilized in the inertia market and FFR (Fast Frequency Response) resource design. Full article

To fully leverage the application potential of distributed energy storage systems (DESS) and network reconfiguration, a coordinated optimization method is proposed to enhance the economic efficiency of distribution networks under normal conditions and the reliability of a power supply during fault conditions. First, a scenario-generation method is developed based on Latin hypercube sampling and Kantorovich distance synchronous back-substitution reduction is used to obtain the typical scenario of wind and solar output. Next, a planning operation coordinated optimization framework and model are established, considering both normal and fault states of the distribution network. In the planning layer, the objective is to minimize the annual comprehensive capital expenditures for the distribution network to improve the economic efficiency of the distribution network. The operation layer includes both normal operation and fault operation states, with the optimization goal of minimizing the sum of normal operation costs and the fault costs associated with load shedding. Subsequently, a hybrid optimization algorithm combining an improved Aquila Optimizer-Second-Order Cone Programming (IAO-SOCP) is proposed to solve the coordinated optimization model. Finally, the proposed coordinated optimization method is validated using an enhanced IEEE 33-bus distribution network case study. The results demonstrate that the method effectively reduces network losses and minimizes load shedding costs during fault conditions, thereby ensuring a balance between the economic efficiency and reliability of the distribution network. Full article