Search Results (41)

Accurate vibration measurement is crucial for maintaining the performance, reliability, and safety of automated manufacturing environments. Abnormal vibrations caused by faults in gears or bearings can degrade positional accuracy, reduce productivity, and, over time, significantly impair production efficiency and product quality. Such vibrations may also disrupt supply chains, cause financial losses, and pose safety risks to workers through collisions, falling objects, or other operational hazards. Conventional vibration measurement techniques, such as wired accelerometers and strain gauges, are typically limited to a few discrete measurement points. Achieving multi-point measurements requires numerous sensors, which increases installation complexity, wiring constraints, and setup time, making the process both time-consuming and costly. The integration of high-frame-rate (HFR) cameras with Digital Image Correlation (DIC) enables non-contact, multi-point, full-field vibration measurement of robot manipulators, effectively addressing these limitations. In this study, HFR cameras were employed to perform non-contact, full-field vibration measurements of industrial robots. The HFR camera recorded the robot’s vibrations at 1000 frames per second (fps), and the resulting video was decomposed into individual frames according to the frame rate. Each frame, with a resolution of 1920 × 1080 pixels, was divided into 128 × 128 pixel blocks with a 64-pixel stride, yielding 435 sub-images. This setup effectively simulates the operation of 435 virtual vibration sensors. By applying mask processing to these sub-images, eight key points representing critical robot components were selected for multi-point DIC displacement measurements, enabling effective assessment of vibration distribution and real-time vibration visualization across the entire manipulator. This approach allows simultaneous capture of displacements across all robot components without the need for physical sensors. The transfer function is defined in the frequency domain as the ratio between the output displacement of each robot component and the input excitation applied by the shaker mounted on the end-effector. The frequency–domain transfer functions were computed for multiple robot components, enabling accurate and full-field vibration analysis during operation. Full article

Serial-parallel hybrid manipulators featuring remote actuation via parallelogram mechanisms are highly valued for their low inertia and high stiffness. However, the complex nonlinear errors introduced by their multi-stage transmission chains pose significant challenges for high-precision calibration. To address this, this paper proposes a hierarchical and decoupled calibration framework specifically tailored for such parallelogram-driven hybrid manipulators. The method first independently calibrates the pose error of the 3-DOF serial main arm using a composite error model that integrates transmission chain constraints. Subsequently, the 2-DOF parallel wrist is accurately calibrated employing a joint-space error identification strategy based on inverse kinematics, thereby circumventing the intractability of solving the parallel mechanism’s forward kinematics. Experimental validation was performed on a self-developed 5-DOF robot prototype using an optical tracker and an attitude sensor. Results from the validation dataset demonstrate that the proposed method reduces the robot’s average positioning error from 2.199 mm to 0.658 mm (a 70.1% improvement) and the average attitude error from 0.8976 deg to 0.1767 deg (an 80.3% improvement). Furthermore, comparative experiments against the standard MDH model and polynomial fitting models confirm that the proposed composite error model and multi-stage transmission error model are essential for achieving high accuracy. This research provides crucial theoretical insights and practical solutions for the high-precision application of complex remote-driven hybrid manipulators. Full article

The multi-finger robotic hand exhibits significant potential in grasping tasks owing to its high degrees of freedom (DoFs). Object grasping results in a closed-chain kinematic system between the hand and the object. This increases the dimensionality of trajectory tracking and substantially raises the computational complexity of traditional methods. Therefore, this study proposes the discrete finite-time convergent neurodynamics (DFTCN) algorithm to address the aforementioned issue. Specifically, a time-varying quadratic programming (TVQP) problem is formulated for each finger, incorporating joint angle and angular velocity constraints through log-sum-exp (LSE) functions. The TVQP problem is then transformed into a time-varying equation system (TVES) problem using the Karush–Kuhn–Tucker (KKT) conditions. A novel control law is designed, employing a three-step Taylor-type discretization for efficient implementation. Theoretical analysis verifies the algorithm’s stability and finite-time convergence property, with the maximum steady-state residual error being $O\left({\tau }^3\right)$. Numerical simulations illustrate the favorable convergence and high accuracy of the DFTCN algorithm compared with three existing dominant neurodynamic algorithms. The real-robot experiments further confirm its capability for precise grasping, even in the presence of camera noise and external disturbances. Full article

The coordination of heterogeneous Unmanned Aerial Vehicles (UAVs) for complex, multi-stage tasks presents a significant challenge in robotics and autonomous systems. Traditional linear models often fail to capture the emergent synergistic effects and dynamic nature of multi-agent collaboration. To address these limitations, this paper proposes a novel hierarchical framework based on a Mission Chain (MC) concept. We systematically define and model key elements of multi-agent collaboration, including Mission Chains (MCs), Execution Paths (EPs), Task Networks (TNs), and Solution Spaces (SSs), creating an integrated theoretical structure. Based on this framework, we formulate the problem as a Sensor–Effector–Target Assignment challenge and propose a Marginal Return-Based Heuristic Algorithm (MRBHA) for efficient dynamic task allocation. Simulations demonstrate that our proposed MRBHA achieves a substantially higher total expected mission value—outperforming standard greedy and random assignment strategies by 14% and 77%, respectively. This validates the framework’s ability to effectively capitalize on synergistic opportunities within the UAV network. The proposed system provides a robust and scalable solution for managing complex missions in dynamic environments, with potential applications in search-and-rescue, environmental monitoring, and intelligent logistics. Full article

In the context of increasingly strong digital transformation and production automation, choosing the right palletizing robot plays a key role in optimizing operational efficiency in industrial chains. However, the wide variety of robot types and specifications complicates decision-making and increases the risk of biased judgments. To overcome this challenge, this study develops an objective multi-criteria decision-making (MCDM) framework that integrates two complementary methods for selecting the optimal industrial pal-letizing robot in the context of modern manufacturing that is increasingly dependent on intelligent automation solutions. Specifically, an improved CRITIC approach is employed to determine objective criteria weights by refining the measurement of contrast intensity and inter-criteria conflict, while normalization ensures comparability of heterogeneous robot parameters. CRADIS is then applied to rank the alternatives based on their relative closeness to the ideal solution. The contributions of this study are twofold: methodological, enhancing the objectivity and robustness of weighting through refined CRITIC and normalization, and practical, offering a reproducible evaluation framework for managers when choosing industrial robots. Application to eight palletizing robots demonstrates that “repeatability” and “power consumption” significantly influence rankings. Sensitivity analysis further confirms the model’s stability and reliability. These findings not only support evidence-based investment decisions but also provide a foundation for extending the method to other industrial technology selection problems. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

This review comprehensively examines the application of deep learning technologies across the entire soybean production chain, encompassing areas such as disease and pest identification, weed detection, crop phenotype recognition, yield prediction, and intelligent operations. By systematically analyzing mainstream deep learning models, optimization strategies (e.g., model lightweighting, transfer learning), and sensor data fusion techniques, the review identifies their roles and performances in complex agricultural environments. It also highlights key challenges including data quality limitations, difficulties in real-world deployment, and the lack of standardized evaluation benchmarks. In response, promising directions such as reinforcement learning, self-supervised learning, interpretable AI, and multi-source data fusion are proposed. Specifically for soybean automation, future advancements are expected in areas such as high-precision disease and weed localization, real-time decision-making for variable-rate spraying and harvesting, and the integration of deep learning with robotics and edge computing to enable autonomous field operations. This review provides valuable insights and future prospects for promoting intelligent, efficient, and sustainable development in soybean production through deep learning. Full article

Robotic search of people in human-centered environments, including healthcare settings, is challenging, as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans, or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person’s plan in an environment. In this paper, we present MLLM-Search, a novel zero-shot person search architecture that leverages multimodal large language models (MLLM) to address the mobile robot problem of searching for a person under event-driven scenarios with varying user schedules. Our approach introduces a novel visual prompting method to provide robots with spatial understanding of the environment by generating a spatially grounded waypoint map, representing navigable waypoints using a topological graph and regions by semantic labels. This is incorporated into an MLLM with a region planner that selects the next search region based on the semantic relevance to the search scenario and a waypoint planner that generates a search path by considering the semantically relevant objects and the local spatial context through our unique spatial chain-of-thought prompting approach. Extensive 3D photorealistic experiments were conducted to validate the performance of MLLM-Search in searching for a person with a changing schedule in different environments. An ablation study was also conducted to validate the main design choices of MLLM-Search. Furthermore, a comparison study with state-of-the-art search methods demonstrated that MLLM-Search outperforms existing methods with respect to search efficiency. Real-world experiments with a mobile robot in a multi-room floor of a building showed that MLLM-Search was able to generalize to new and unseen environments. Full article

Parallel robots, including cable-driven parallel robots (CDPRs), are widely used due to their high stiffness, precision, and high dynamic performance. However, their multi-chain closed-loop architecture brings nonlinear, multi-degree-of-freedom coupled motion and sensitivity to geometric errors, which result in significant challenges in their modeling, error compensation, and control. The rise in machine learning technology has provided a promising approach to address these issues by learning complex relationships from data, enabling real-time prediction, compensation, and adaptation. This paper reviews the progress of typical applications of machine learning methods in parallel robots, covering four main areas: kinematic modeling, error compensation, trajectory tracking control, as well as other emerging applications such as design synthesis, motion planning, and CDPR fault diagnosis. The key technologies used, their implementation architecture, technical difficulties solved, performance advantages and applicable scope are summarized. Finally, the review outlines current challenges and future directions. It is proposed that hybrid learning physics modeling, transfer learning, lightweight deployment, and interdisciplinary collaboration will be the key directions for advancing the integration of machine learning and parallel robotic systems. Full article

Nowadays, the security and stability of the global industrial chain are facing unprecedented challenges. In this context, understanding how industrial robots affect chain resilience is key to promoting high-quality economic development. This study focuses on 104 cities in the Yangtze River Delta, using data from 2006 to 2021. It applies a threshold regression model and a spatial Durbin model to examine how industrial robot penetration drives chain resilience and how its effects spread across regions. The results reveal three main findings: First, the integration of advanced manufacturing and modern services, together with strong urban innovation capacity, plays a significant role in enhancing industrial chain resilience. Second, this study evaluates policy experiments, such as Low-Carbon City and Broadband China initiatives, using a multi-period difference-in-differences model. The findings show that pilot cities involved in these programs demonstrate higher levels of chain resilience. Third, the relationship between robot penetration and chain resilience shows clear spatial and temporal patterns. Cities with higher robot usage tend to drive development in surrounding areas. This, in turn, encourages more intensive production and fosters stronger coordination across industries. Overall, this study contributes to the growing body of research on chain resilience. More importantly, it offers practical policy insights. Governments and regional firms can work together to build a new development model that enhances resilience and supports long-term economic stability. Full article

This research addresses the problem of the initial synthesis of kinematic chains with spherical kinematic pairs, which are essential in the design of spatial mechanisms used in robotics, aerospace, and mechanical systems. The goal is to establish the existence of solutions for defining the geometric and motion constraints of these kinematic chains, ensuring that the synthesized mechanism achieves the desired motion with precision. By formulating the synthesis problem in terms of nonlinear algebraic equations derived from the spatial positions and orientations of the links, we analyze the conditions under which a valid solution exists. We explore both analytical and numerical methods to solve these equations, highlighting the significance of parameter selection in determining feasible solutions. Specifically, our approach demonstrates the visualization of fixed points, such as A, B, and C, alongside their spatial differences with respect to reference points and transformation matrices. We detail methods for plotting transformation components, including rotation matrix elements (e, m, and n) and derived products from these matrices, as well as the representation of angular parameters (θ_i, ψ_i, and $\phi$_i) in a three-dimensional context. The proposed techniques not only facilitate the debugging and analysis of complex kinematic behaviors but also provide a flexible tool for researchers in robotics, computer graphics, and mechanical design. By offering a clear and interactive visualization strategy, this framework enhances the understanding of spatial relationships and transformation dynamics inherent in multi-body systems. Full article

Inspired by changing the operation mode via branch-chain switching, a symmetrical multi-arm robot is proposed to meet the demand of continuous high-performance output. The kinematics and Jacobian matrix of the mechanism are established and solved, and the parameter expression when singularity occurs is obtained. As Type-I singularity is the key limiting factor of continuous motion, a branch-chain switching and motion planning method is proposed. Numerical simulation and joint interpolation control are explained according to the pseudo-inverse matrix. The mechanism completes the switching between the executing branch chain and the branch chain to be executed to realize continuous rotation with a large angle. The results prove the feasibility of the design and the correctness of the model, proving that this method can be a reference method for the design of this kind of robot. Full article

Parallel-kinematic machines or parallel robots have only been established in a few applications where their advantage over serial kinematics due to their high payload capacity, stiffness, or dynamics with their limited workspace-to-installation-space ratio pays out. However, some applications still have not yet been sufficiently or satisfactorily automated in which parallel robots could be advantageous. As their performance is much more dependent on their complex dimensioning, an automated design tool—not existing yet—is required to optimize the parameterization of parallel robots for applications. Combined structural and dimensional synthesis considers all principally possible kinematic structures and performs a separate dimensioning for each to obtain the best task-specific structure. However, this makes the method computationally demanding. The proposed computationally efficient approach for dimensional synthesis extends multi-objective particle swarm optimization with hierarchical constraints. A cascaded (bilevel) optimization includes the design optimization of components and the redundancy resolution for tasks with rotational symmetry, like milling. Two case studies for different end-effector degrees of freedom demonstrate the broad applicability of the combined structural and dimensional synthesis for symmetric parallel robots with rigid links and serial-kinematic leg chains. The framework produces many possible task-optimal structures despite numerous constraints and can be applied to other problems as an open-source Matlab toolbox. Full article

The accurate detection of litchi fruit cluster is the key technology of litchi picking robot. In the natural environment during the day, due to the unstable light intensity, uncertain light angle, background clutter and other factors, the identification and positioning accuracy of litchi fruit cluster is greatly affected. Therefore, we proposed a method to detect litchi fruit cluster in the night environment. The use of artificial light source and fixed angle can effectively improve the identification and positioning accuracy of litchi fruit cluster. In view of the weak light intensity and reduced image features in the nighttime environment, we proposed the YOLOv8n-CSE model. The model improves the recognition of litchi clusters in night environment. Specifically, we use YOLOv8n as the initial model, and introduce the CPA-Enhancer module with chain thinking prompt mechanism in the neck part of the model, so that the network can alleviate problems such as image feature degradation in the night environment. In addition, the VoVGSCSP design pattern in Slimneck was adopted for the neck part, which made the model more lightweight. The multi-scale linear attention mechanism and the EfficientViT module, which can be deeply divided, further improved the detection accuracy and detection rate of YOLOv8n-CSE. The experimental results show that the proposed YOLOv8n-CSE model can not only recognize litchi clusters in the night scene, but also has a significant improvement over previous models. In mAP@0.5 and F1, YOLOv8n-CSE achieved 98.86% and 95.54% respectively. Compared with the original YOLOv8n, RT-DETR-l and YOLOv10n, mAP@0.5 is increased by 4.03%, 3.46% and 3.96%, respectively. When the number of parameters is only 4.93 m, F1 scores are increased by 5.47%, 2.96% and 6.24%, respectively. YOLOv8n-CSE achieves an inference time of 36.5ms for the desired detection results. To sum up, the model can satisfy the criteria of the litchi cluster detection system for extremely accurate nighttime environment identification. Full article

Traditional mobile robots have limited mobility in complex terrain environments. Generally, the closed-chain leg structure of a foot-type robot relies on the speed difference to turn, but it is difficult to complete the turning action in narrow spaces. Therefore, this study proposes a closed-chain foot-type robot that can move in multiple directions, inspired by the WATT-I leg structure. Firstly, the closed-chain single-leg structure is designed, and the leg structure is analyzed in terms of the degrees of freedom, kinematics, and singularity. A simulation is also carried out. Secondly, based on the present trajectory, a heuristic algorithm is used to solve the inverse trajectory problem, and the size of the mechanism is optimized. Finally, the steering mechanism of the leg with a zero turning radius is designed and analyzed, which achieves the steering function of the whole robot and satisfies the goal of enabling the foot robot to walk in all directions. This study provides theoretical guidance for the structural dimension optimization of the proposed foot mobile robot and its application in engineering fields such as rescue, exploration, and the military. Full article