Search Results (2,820)

This study aims to help researchers quickly understand the latest research status of kiwifruit picking robots to expand their research ideas. The centralized picking of kiwifruit is confronted with challenges such as high labor intensity and labor shortage. A series of social issues including the decline in agricultural population and population aging have further increased the cost of its harvest. Therefore, intelligent picking robots replacing manual operations is an effective solution. This paper, through literature review and organization, analyzes and evaluates the performance characteristics of various current kiwifruit picking robots. It summarizes the key technologies of kiwifruit picking robots, from the aspects of robot vision systems, mechanical arms, and the end effector. At the same time, it conducts an in-depth analysis of the problems existing in automatic kiwifruit harvesting technology in modern agriculture. Finally, it is concluded that in the future, research should be carried out in aspects such as kiwifruit cluster recognition algorithms, picking efficiency, and damage cost and universality to enhance the operational performance and market promotion potential of kiwifruit picking robots. The significance of this review lies in addressing the imminent labor crisis in agricultural production and steering agriculture toward intelligent and precise transformation. Its contributions are reflected in greatly advancing robotic technology in complex agricultural settings, generating substantial technical achievements, injecting new vitality into related industries and academic fields, and ultimately delivering sustainable economic benefits and stable agricultural supply to society. Full article

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based simulated environments demonstrated that when benchmarked against the classical MARL baseline, QI-MARL achieved an 8% improvement in path efficiency, a 12% increase in task success rate, and a 15% reduction in energy consumption. When compared with the rule-based approach, improvements reached 15%, 20%, and 26%, respectively. Ablation studies further confirmed the substantial contributions of the quantum-inspired exploration and energy-sharing mechanisms, while sensitivity and scalability analyses validated the system’s robustness across varying swarm sizes and environmental complexities. The proposed framework effectively integrates quantum-inspired AI, intelligent microgrid management, and autonomous robotics, offering a novel approach to energy coordination in cyber-physical systems. Potential applications include smart buildings, industrial campuses, and distributed renewable energy networks, where the system enables flexible, resilient, and energy-efficient robotic operations within modern electrical engineering contexts. Full article

Motion planning in robotic systems, particularly in industrial contexts, must balance execution speed, precision, and safety. Excessive accelerations, especially centripetal ones in high, curvature regions, can cause vibrations, reduce tracking accuracy, and increase mechanical wear. This paper presents an off-line motion planning method that integrates curvature-based velocity modulation with jerk- and acceleration-limited S-curve profiles. The approach autonomously adjusts the speed along a predefined path according to local curvature by planning the motion at piecewise constant velocity and ensuring compliance with dynamic constraints on jerk, acceleration, and velocity. A non-linear filter tracks the velocity reference and smooths transitions while maintaining fluid motion, automatically adjusting velocity based on path curvature, ensuring smooth S-curve trajectories without requiring manual intervention. By jointly addressing geometric feasibility and dynamic smoothness, the proposed method reduces execution time while minimizing vibrations in applications involving abrupt curvature variations, as confirmed by its application to planar and spatial trajectories with varying curvature complexity. The method applies to smooth parametric trajectories and is not intended for paths with tangent discontinuities. The simulation results confirm full compliance with the imposed acceleration and jerk limits; nevertheless, future work will include experimental validation on realistic process trajectories and a quantitative performance assessment. Full article

This study explores the use of construction robots with collaborative path planning and coordination in complex building construction tasks. Current construction processes involving robots are often fragmented due to their single-task focus, with limited research focused on employing multiple construction robots to collaboratively perform tasks. To address such a challenge, this research proposes an improved A* algorithm for global path planning and obstacle avoidance, combined with the development of a BIM-based grid map of the construction site. The leader–follower method is utilized to guide the robot group in maintaining an optimal formation, ensuring smooth collaboration during construction. The methodology includes formalizing building construction site environments into BIM-based grid maps, path planning, and obstacle avoidance, which allows robot groups to autonomously navigate and complete specific tasks such as concrete, masonry, and decoration construction. The results of this study show that the proposed approach achieves significant reductions in pathlength and operational time of approximately 9% and 10%, respectively, while maintaining safety and efficiency compared with traditional manual methods. This research demonstrates the potential of collaborative construction robot groups to enhance productivity, reduce labor costs, and provide a scalable solution for the intelligent transformation of the construction industry; extends the classical A* algorithm by incorporating obstacle density into the heuristic function; and proposes a new node simplification strategy, contributing to the literature on robot motion planning in semi-structured environments. Full article

Robot manipulators are suitable for many industrial tasks, such as assembly and pick-and-place operations. However, high-acceleration motions result in shaking forces and moments to the base, which can cause vibration of the manipulator and instability in the case of a mobile base. Furthermore, gravity compensation of the manipulator links requires additional motor torque, which can increase energy consumption. Balanced manipulators address these problems by employing a mechanical design that results in the balancing of gravity and other static forces, or the removal of shaking forces and/or moments. This review paper provides an overview of mechanical design approaches for balanced robotic manipulation, with an emphasis on experimentally prototyped designs. We first define the types of balancing according to the literature. We then provide an overview of different approaches to the mechanical design of balanced manipulators, along with simple examples of their implementation. Experimental prototypes in this field are then comprehensively presented and summarized to allow readers to compare their development maturity. At the end of the paper, we outline challenges and future directions of research. Full article

Multi-robot ensembles comprising several manipulators are commonly used in industrial settings to process non-deterministic flows of items loaded by an upstream source onto a shared transportation system. After the execution of a given task, the robots regularly deposit the items on a common output flow, which conveys the semi-finished material towards the downstream portion of the plant for further processing. The productivity and reliability of the entire process, which is affected by the plant layout, by the quality of the adopted scheduling and task assignment algorithms, and by the proper balancing of the input and output flows, may be degraded by random disturbances and transient conditions of the input flow. In this paper, a highly accurate event-based simulator of this kind of system is used in conjunction with a rollout algorithm to optimize the performance of the plant in all operating scenarios. The proposed method relies on a simulation of the plant that comprehensively considers the dynamic performance of the manipulators, their actual motion planning algorithms, the adopted scheduling and task assignment methods, and the regulation of the material flows. The simulation environment is built upon computationally efficient maps able to predict the execution time of the tasks assigned to the robots, considering all the determining factors, and on a representation of the manipulators themselves as finite state automata. The proposed formalization of the line balancing problem as a Markov Decision Process and the resulting rollout optimization method are shown to substantially improve the performance of the plant, even in challenging situations, and to be well suited to real-time implementation even on commodity hardware. Full article

In many industrial facilities, online monitoring systems have improved the reliability of key equipment, reducing the cost of operation and maintenance over recent decades. However, it often requires additional on-site inspection of target facilities due to limited information from installed sensors. To systematically automate such processes, an adaptive online fault diagnosis framework is required, which consecutively selects variables to measure and updates its inference with additional information at each measurement step. In this paper, adaptive online fault detection models—grafted composite decision trees—are proposed for such a framework. While conventional decision trees themselves can serve two required objectives of the framework, information from monitored variables can be less utilized because decision trees do not consider if required input variables are always monitored when the models are trained. On the other hand, the proposed grafted composite decision tree models are designed to fully utilize both monitored and robot-measured variables at any stage in a given measurement sequence by grafting two types of trees together: a prior-tree trained only with observed variables and sub-trees trained with robot-measurable variables. The proposed method was validated on a cooling water system in a nuclear power plant with multiple leak scenarios, in which improved measurement selection and increase in inference confidence in each measurement step are demonstrated. The performance comparison between the proposed models and the conventional decision tree model clearly illustrates how the acquired information is fully utilized for the best inference while providing the best choice of the next variable to measure, maximizing information gain at the same time. Full article

Smart devices and emerging technologies are highly popular devices and technologies that considerably improve our daily living by reducing or replacing human workforces, treating disease, monitoring healthcare, enhancing service performance, improving quality, and protecting the natural environment, and promoting non-gas emissions, sustainable working, green technologies, and renewable energy. Triboelectric nanogenerators (TENGs) have recently emerged as a type of advanced energy harvesting technology that is simple, green, renewable, flexible, and endurable as an energy resource. High-performance TENGs, denoted as advanced TENGs, have potential for use in many practical applications such as in self-powered sensors and sources, portable electric devices, power grid penetration, monitoring manufacturing processes for quality control, and in medical and healthcare applications that meet the criteria for smart devices and emerging technologies. Advanced TENGs are used as highly efficient energy harvesters that can convert many types of wasted mechanical energy into the electric energy used in a range of practical applications in our daily lives. This article reviews recently advanced TENGs and their potential for use with smart devices and emerging technology applications. The work encourages and strengthens motivation to develop new smart devices and emerging technologies to serve us in many fields of our daily living. When TENGs are introduced into smart devices and emerging technologies, they can be applied in a variety of practical applications such as the food processing industry, information and communication technology, agriculture, construction, transportation, marine technology, the energy sector, mechanical processing, manufacturing, self-powered sensors, Industry 4.0, drug safety, and robotics due to their sustainable and renewable energy, light weight, cost effectiveness, flexibility, and self-powered portable energy sources. Their advantages, disadvantages, and solutions are also discussed for further research. Full article

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions in power consumption while preserving navigation reliability. The proposed system is implemented on a low-power microcontroller and evaluated through simulations and prototype testing in constrained channel environments. Results show a 35% reduction in energy consumption with minimal impact on navigation performance, demonstrating the framework’s effectiveness for long-duration autonomous operations in pipelines, sewers, and industrial ducts. Full article

Weld seam detection is a fundamental prerequisite for robotic welding automation, yet it remains challenging due to the elongated shape of welds, weak contrast against metallic backgrounds, and significant environmental interference in industrial scenarios. To address these challenges, we propose a novel deep neural network architecture termed SANet (Strip-Aware Network). The model is constructed upon a U-shaped backbone and integrates strip-aware feature modeling with multistage supervision. It mainly consists of two complementary modules: the Paralleled Strip and Spatial Context-Aware (PSSCA) module and the Multistage Fusion (MF) module. The PSSCA module enhances the extraction of elongated strip-like features by combining parallel strip perception with spatial context modeling, thereby improving fine-grained weld seam representation. In addition, SANet integrates the StripPooling attention mechanism as an auxiliary component to enlarge the receptive field along strip directions and enhance feature discrimination under complex backgrounds. Meanwhile, the MF module performs cross-stage feature fusion by aggregating encoder and decoder features at multiple levels, ensuring accurate boundary recovery and robust global-to-local interaction. The weld seam detection task is formulated as a two-dimensional segmentation problem and evaluated on a self-built dataset consisting of over 4000 weld seam images covering diverse industrial scenarios such as pipe joints, trusses, elbows, and furnace structures. Experimental results show that SANet achieves an IoU of 96.23% and a Dice coefficient of 98.07%, surpassing all compared models and demonstrating its superior performance in weld seam detection. These findings validate the effectiveness of the proposed architecture and highlight its potential as a low-cost, flexible, and reliable pure vision solution for intelligent welding applications. Full article

The swift integration of AI, robotics, and advanced sensing technologies has revolutionized agriculture into a data-centric, autonomous, and sustainable sector. This systematic study examines the interplay between artificial intelligence and agricultural robotics in intelligent farming systems. Artificial intelligence, machine learning, computer vision, swarm robotics, and generative AI are analyzed for crop monitoring, precision irrigation, autonomous harvesting, and post-harvest processing. Employing PRISMA to categorize more than 10,000 high-impact publications from Scopus, WoS, and IEEE. Drones and vision-based models predominate the industry, while IoT integration, digital twins, and generative AI are on the rise. Insufficient field validation rates, inadequate crop and regional representation, and the implementation of explainable AI continue to pose significant challenges. Inadequate model generalization, energy limitations, and infrastructural restrictions impede scalability. We identify solutions in federated learning, swarm robotics, and climate-smart agricultural artificial intelligence. This paper presents a framework for inclusive, resilient, and feasible AI-robotic agricultural systems. Full article

Welding-based manufacturing and joining processes are extensively used in various areas of industrial production. While welding has been used as a primary method of joining in many applications, its capability to fabricate metal components such as the Wire Arc Additive Manufacturing (WAAM) method should not be undermined. WAAM is a promising method for producing large metal parts, but it is still prone to defects such as porosity that can reduce structural reliability. To ensure these defects are found and measured in a consistent way, inspection methods must be tied directly to code-based acceptance limits. In this work, a three-pass WAAM joint specimen was made in a welded-joint configuration using robotic GMAW-based deposition. This setup provided a stable surface for Phased Array Ultrasonic Testing (PAUT) while still preserving WAAM process conditions. The specimen, which was intentionally seeded with porosity, was divided into five zones and inspected using the 6 dB drop method for defect length and amplitude-based classification, with AWS D1.5 serving as the reference code. The results showed that porosity was not uniform across the bead. Zones 1 and 3 contained the longest clusters (15 mm and 16.5 mm in length) and exceeded AWS length thresholds, while amplitude-based classification suggested they were less critical than other regions. This difference shows the risk of relying on only one criterion. By embedding these results in a DMAIC (Define–Measure–Analyze–Improve–Control) workflow, the inspection outcomes were linked to likely causes such as unstable shielding and cooling effects. Overall, the study demonstrates a code-referenced, dual-criteria approach that can strengthen quality control for WAAM. Full article

The advancement of adaptable industrial robots in intelligent manufacturing is hindered by the inefficiency of traditional motion planning methods in high-dimensional spaces. Therefore, a Dempster–Shafer evidence theory-based hybrid motion planner is proposed, in which a probabilistic sampling network (PSNet) and an enhanced artificial potential field (EAPF) cooperate with each other to improve the planning performance. The PSNet architecture comprises two modules: a motion planning module (MPM) and a fusion sampling module (FSM). The MPM utilizes sensor data alongside the robot’s current and target configurations to recursively generate diverse multimodal distributions of the next configuration. Based on the distribution information, the FSM was used as a decision-maker to ultimately generate globally connectable paths. Moreover, the FSM is equipped to correct collision path points caused by network inaccuracies through Gaussian resampling. Simultaneously, an augmented artificial potential field with a dynamic rotational field is deployed to repair local paths when worst-case collision scenarios occur. This collaborative strategy harmoniously unites the complementary strengths of both components, thereby enhancing the overall resilience and adaptability of the motion planning system. Experiments were conducted in various environments. The results demonstrate that the proposed method can quickly find directly connectable paths in diverse environments while reliably avoiding sudden obstacles. Full article

The adoption of robots in the construction phase can improve safety by replacing hazardous tasks and enhance productivity by automating repetitive work. Despite these advantages, adoption remains slow, constrained by economic, industrial, institutional, socio-cultural, and technological barriers. Wider acceptance is particularly urgent in construction, where fragmented processes, low profit margins, and safety risks make innovation both necessary and challenging. This study identified 22 critical barriers through a systematic literature review and categorized them into five dimensions. Beyond identification, the study prioritized these barriers using ISM and MICMAC analysis, clarifying which factors are fundamental drivers and which are outcome-related. The results showed that economic drivers occupy the base of the hierarchy and exert the greatest systemic influence, socio-cultural barriers emerge as highly dependent outcomes, and software usability acts as a linkage factor connecting technological immaturity with social acceptance. These findings reveal that barriers are interdependent rather than isolated and underscore the need for a structured prioritization framework. By applying ISM and MICMAC, this study presents a stepwise roadmap that differentiates fundamental drivers from outcome-related constraints, offering academic insights and practical guidance for policymakers to design strategies such as investment incentives, standardization, legal frameworks, and R&D expansion to accelerate adoption. Full article

The construction industry faces persistent productivity shortfalls and rising carbon dioxide emissions, which drives a shift toward the use of low-carbon materials and higher degrees of automation. Timber, a renewable and carbon-sequestering material, becomes especially compelling when combined with robotic fabrication. Although rapid advances have been implemented in the last decade, research and practice remain fragmented, and systematic evaluations of technological readiness are scarce. This gap is addressed in this review through critical literature synthesis of robotic timber construction, combining bibliometric analysis with a comparative evaluation of twelve representative case studies from 2020 to 2025. Computational and robotic tools are mapped across the design to fabrication pipeline, and emerging advancements are identified such as digital twins, real-time adaptive workflows, and machine learning driven fabrication, alongside discrete and circular strategies. Barriers to scale up are also assessed, including mid-level technology readiness, regulatory and safety obligations for human–robot interaction, evidence on cost and productivity, and workforce training needs. By clarifying the current level of robotization and specifying both research gaps and industrial prerequisites, this study provides a structured foundation for the next phase of development. It helps scholars by consolidating methods and metrics for rigorous evaluation, and it helps practitioners by highlighting pathways to scalable, certifiable, and circular deployment that align cost, safety, and training requirements. Full article