Search Results (133)

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

Background: Drones are rapidly establishing themselves as one of the most critical technologies. Robotics, automated machinery, intelligent manufacturing, and other high-impact technological research and applications bring up pressing ethical, social, legal, and political issues. Methods: The present research aims to present the results of a qualitative investigation that looked at perceptions of the growing socio-ethical conundrums surrounding the development of drone applications. Results: According to the obtained results, participants often share similar opinions about whether different drone applications are approved by the public, regardless of their level of experience. Perceptions of drone applications appear consistent across various levels of expertise. The most notable associations are with military objectives (73%), civil protection (61%), and passenger transit and medical purposes (56%). Applications that have received high approval include science (8.70), agriculture (8.78), and disaster management (8.87), most likely due to their obvious social benefits and reduced likelihood of ethical challenges. Conclusions: The study’s findings can help shape the debate on drone acceptability in particular contexts, inform future research on promoting value-sensitive development in society more broadly, and guide researchers and decision-makers on the use of drones, as people’s attitudes, understanding, and usage will undoubtedly impact future advancements in this technology. Full article

This study addresses the ’last-mile’ transportation challenges that arise in steep and narrow forest terrain by proposing a novel robotic palanquin system called roCaGo. It is inspired by the mechanical principles of two-wheel-steering and two-wheel-drive (2WS/2WD) bicycles. The roCaGo system integrates front- and rear-wheel-drive mechanisms, as well as a central suspension structure for carrying loads. Unlike conventional forestry machinery, which requires wide, well-maintained roads or permanent rail systems, the roCaGo system enables flexible, operator-assisted transport along narrow, unprepared mountain paths. A dynamic model of the system was developed to design a stabilization control strategy, enabling roCaGo to maintain transport stability and assist the operator during navigation. Numerical simulations and preliminary physical experiments demonstrate its effectiveness in challenging forest environments. Furthermore, the applicability of roCaGo has been extended to include use as a mobile third-person viewpoint platform to support the remote operation of existing forestry equipment; specifically the LV800crawler vehicle equipped with a front-mounted mulcher. Field tests involving LiDAR sensors mounted on roCaGo were conducted to verify its ability to capture the environmental data necessary for non-line-of-sight teleoperation. The results show that roCaGo is a promising solution for improving labor efficiency and ensuring operator safety in forest logistics and remote-controlled forestry operations. Full article

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production. Full article

At present, there is a lack of effective and usable machinery in the harvesting of aquatic vegetables. The harvesting of most aquatic vegetables such as Brasenia schreberi relies entirely on manual labor, resulting in a high labor demand and labor shortages, which restricts the industrial development of aquatic vegetables. To address this problem, an intelligent harvesting system for the aquatic vegetable Brasenia schreberi was developed in response to the challenging working conditions associated with harvesting it. The system is composed of a catamaran mobile platform, a picking device, and a harvesting manipulator control system. The mobile platform, driven by two paddle wheels, is equipped with a protective device to prevent vegetable stem entanglement, making it suitable for shallow pond aquatic vegetable environments. The self-designed picking device rapidly harvests vegetables through lateral clamping and cutting. The harvesting manipulator control system incorporates harvesting posture perception based on the YOLO-GS recognition algorithm and combines it with an improved RRT algorithm for robotic arm path planning. The experimental results indicate that the intelligent harvesting system is suitable for aquatic vegetable harvesting and the improved RRT algorithm surpasses the traditional one in terms of the planning time and path length. The vision-based positioning error was 4.80 mm, meeting harvesting accuracy requirements. In actual harvest experiments, the system showed an average success rate of 90.0%, with an average picking time of 5.229 s per leaf, thus proving its feasibility and effectiveness. Full article

Polyvinyl chloride (PVC) recycling poses significant engineering challenges and opportunities, particularly regarding material integrity, energy efficiency, and integration into circular manufacturing systems. This systematic review evaluates recent advancements in mechanical innovations, tooling strategies, and intelligent technologies reshaping PVC recycling. An emphasis is placed on machinery-driven solutions—including high-efficiency shredders, granulators, extrusion moulders, and advanced sorting systems employing hyperspectral imaging and robotics. This review further explores chemical recycling technologies, such as pyrolysis, gasification, and supercritical fluid extraction, for managing contamination and additive removal. The integration of Industry 4.0 technologies, notably digital twins and artificial intelligence, is highlighted for its role in predictive maintenance, real-time quality assurance, and process optimisation. A combined PRISMA approach and ontological mapping are applied to classify technological pathways and lifecycle optimisation strategies. Critical engineering constraints—including thermal degradation, additive leaching, and feedstock heterogeneity—are examined alongside emerging innovations, like additive manufacturing and microwave-assisted depolymerisation, offering scalable, low-emission solutions. Regulatory instruments, such as REACH and Extended Producer Responsibility (EPR), are analysed for their influence on machinery compliance and design standards. Drawing from sustainable manufacturing frameworks, this study also promotes energy efficiency, eco-designs, and modular integration in recycling systems. This paper concludes by proposing a digitally optimized, machinery-integrated recycling model aligned with circular economy principles to support the development of future-ready PVC reprocessing infrastructures. This review serves as a comprehensive resource for researchers, practitioners, and policymakers, advancing sustainable polymer recycling. Full article

In this work, lead-free (Na_0.475K_0.475Li_0.05)NbO₃ + x wt.% ZnO (NKLN, x = 0 to 0.3) piezoelectric ceramics with high piezoelectric g-coefficients were prepared by conventional solid-state synthesis method. By adding different concentrations of ZnO dopants, we aimed to improve the material properties and enhance their piezoelectric properties. The effects of the ZnO addition on the microstructure, dielectric, piezoelectric and ferroelectric properties of the proposed samples are investigated. Adding ZnO reduced the dielectric constant and improved the g-value of the samples. The properties of the samples without ZnO doping were g₃₃ = 31 mV·m/N, g₁₅ = 34 mV·m/N, k_p = 0.39, Q_m = 92, ε_r = 458, d₃₃ = 127 pC/N and dielectric loss = 3.4%. With the preferable ZnO doping of 1 wt.%, the piezoelectric properties improved to g₃₃ = 40 mV·m/N, g₁₅ = 44 mV·m/N, k_p = 0.44, Q_m = 89, ε_r = 406, d₃₃ = 139 pC/N and dielectric loss = 2.4%. Finally, ring-shaped shear mode piezoelectric accelerometers were fabricated using the optimum ZnO-doped samples. The simulated resonant frequency using ANSYS 2024 R1 software was approximately 23 kHz, while the actual measured resonant frequency of the devices was 19 kHz. The sensitivity was approximately 7.08 mV/g. This piezoelectric accelerometer suits applications requiring lower sensitivity and higher resonant frequencies, such as monitoring high-frequency vibrations in high-speed machinery, robotic arms or scientific research and engineering fields involving high-frequency vibration testing. Full article

Advances in industrial 5G communication technologies and robotics create new possibilities while also increasing the complexity and variability of networked control systems. The additional throughput and lower latency provided by 5G networks enable applications such as teleoperation of machinery, flexible reconfigurable robotic manufacturing cells, or automated guided vehicles. These use cases are set up in dynamic network environments where communication latency and jitter become critical factors that must be managed. Despite the advancements in 5G technologies, such as ultra-reliable low-latency communication (URLLC), adaptive control strategies such as reinforcement learning (RL) remain critical to handle unpredictable network conditions and ensure optimal system performance in real-world industrial applications. In this paper, we investigate the potential of RL in scenarios with communication latency similar to a public 5G deployment. Our study includes an incremental improvement by utilizing long short-term memory-based neural networks in combination with proximal policy optimization in this scenario. Our findings indicate that incorporating latency into the training environment enhances the robustness and efficiency of RL controllers, especially in scenarios characterized by variable network delays. This exploration provides insights into the feasibility of using RL for networked control systems and underscores the importance of incorporating realistic network conditions into the training phase. Full article

Automating agricultural machinery presents a significant opportunity to lower costs and enhance efficiency in both current and future field operations. The detection and destruction of weeds in agricultural areas via robots can be given as an example of this process. Deep learning algorithms can accurately detect weeds in agricultural fields. Additionally, robotic systems can effectively eliminate these weeds. However, the high computational demands of deep learning-based weed detection algorithms pose challenges for their use in real-time applications. This study proposes a vision-based autonomous agricultural robot that leverages the YOLOv8 model in combination with ByteTrack to achieve effective real-time weed detection. A dataset of 4126 images was used to create YOLO models, with 80% of the images designated for training, 10% for validation, and 10% for testing. Six different YOLO object detectors were trained and tested for weed detection. Among these models, YOLOv8 stands out, achieving a precision of 93.8%, a recall of 86.5%, and a mAP@0.5 detection accuracy of 92.1%. With an object detection speed of 18 FPS and the advantages of the ByteTrack integrated object tracking algorithm, YOLOv8 was selected as the most suitable model. Additionally, the YOLOv8-ByteTrack model, developed for weed detection, was deployed on an agricultural robot with autonomous driving capabilities integrated with ROS. This system facilitates real-time weed detection and destruction, enhancing the efficiency of weed management in agricultural practices. Full article

Industry 5.0 aims to integrate humans and machines to achieve greater productivity, personalization, and sustainable development in the production process. Built on the foundation of Industry 4.0 which emphasizes automation, digitalization, and intelligent production processes, Industry 5.0 highlights the importance of human resources in modern manufacturing. Robotic arms have replaced traditional manpower, particularly in flexible manufacturing systems. However, integrating advanced machinery into workflows has increased competition in terms of securing resources, resulting in frequent deadlocks. Various deadlock prevention policies have been proposed to address this issue. Despite these efforts, resolving system deadlocks while achieving the optimal number of reachable states remains challenging. Based on existing research, we have developed a novel deadlock recovery method applicable to various flexible manufacturing systems. We designed an adaptable system and a controller that can restore the system to its fully operational state. Full article

Accurate rice row detection is critical for autonomous agricultural machinery navigation in complex paddy environments. Existing methods struggle with terrain unevenness, water reflections, and weed interference. This study aimed to develop a robust rice row detection method by integrating multi-sensor data and leveraging robot travelling prior information. A 3D point cloud acquisition system combining 2D LiDAR, AHRS, and RTK-GNSS was designed. A variable-threshold segmentation method, dynamically adjusted based on real-time posture perception, was proposed to handle terrain variations. Additionally, a clustering algorithm incorporating rice row spacing and robot path constraints was developed to filter noise and classify seedlings. Experiments in dryland with simulated seedlings and real paddy fields demonstrated high accuracy: maximum absolute errors of 59.41 mm (dryland) and 69.36 mm (paddy), with standard deviations of 14.79 mm and 19.18 mm, respectively. The method achieved a 0.6489° mean angular error, outperforming existing algorithms. The fusion of posture-aware thresholding and path-based clustering effectively addresses the challenges in complex rice fields. This work enhances the automation of field management, offering a reliable solution for precision agriculture in unstructured environments. Its technical framework can be adapted to other row crop systems, promoting sustainable mechanization in global rice production. Full article

The agricultural sector is currently facing problems including a decline in the agricultural population, labor shortages, and an aging population. To solve these problems and increase agricultural productivity, the development and distribution of autonomous agricultural machinery is necessary. Since autonomous agricultural machinery is operated along a pre-defined path, it is essential to generate an autonomous driving path that takes into account the driving and working methods of the agricultural machinery. In this study, an autonomous driving path-generation algorithm for the autonomous operation of a crawler-type ridge-forming robot is proposed. The proposed algorithm defines the field boundary using the geodetic coordinates of the field boundary points and the size of the robot, generates working line segments within the field boundary, and generates three types of waypoints, which constitute an autonomous driving path based on the autonomous driving operating scenario. To verify the proposed algorithm, tests were conducted using four types of field boundary points with different shapes, and the results are presented. As a result of the simulation test, when a ridge was created using the generated autonomous driving path, the area occupied by the ridge in the total field area according to the field types of a rectangle, trapezoid, pentagon, and hexagon was indicated to be 80, 77, 85, and 77%, respectively. Full article

This article provides a comprehensive review of the current results of pear-picking technology, delving into the development process, classification, application status, and development trends of picking machinery, picking robots, and intelligent technology. By analyzing the key technologies in pear fruit harvesting, this paper explores the working principles of harvesting machinery, the technical characteristics of harvesting robots, and the potential applications of intelligent technology. Furthermore, a bibliometric analysis was employed to examine two decades of the research literature on Korla fragrant pear, spanning from January 2004 to June 2024, utilizing the core collection of the Web of Science and the China National Knowledge Infrastructure database as the retrieval platforms. The visualization of the analysis results indicates that the focal points of research in this field are predominantly aspects such as the quality and storage conditions of fragrant pears, with a scarcity of studies directed toward mechanized harvesting. Additionally, this study addresses the existing challenges and issues within pear-picking technology and delineates potential avenues for future development, with the objective of providing a foundation for subsequent research on Korla fragrant pear-harvesting technology. Full article

The decline in the number of essential farmers has become a significant issue in Japanese agriculture. In response, there is increasing interest in the electrification and automation of agricultural machinery, particularly in relation to the United Nations Sustainable Development Goals (SDGs). This study focuses on the development of an electric vehicle (EV) crawler-type robot designed for weed cultivation operations, with the aim of reducing herbicide use in organic onion farming. Weed cultivation requires precise, delicate operations over extended periods, making it a physically and mentally demanding task. To alleviate the labor burden associated with weeding, we employed a color camera to capture crop images and used artificial intelligence (AI) to identify crop rows. An automated system was developed in which the EV crawler followed the identified crop rows. The recognition data were transmitted to a control PC, which directed the crawler’s movements via motor drivers equipped with Controller Area Network (CAN) communication. Based on the crop row recognition results, the system adjusted motor speed differentials, enabling the EV crawler to follow the crop rows with a high precision. Field experiments demonstrated the effectiveness of the system, with automated operations maintaining a lateral deviation of ±2.3 cm, compared to a maximum error of ±10 cm in manual operation. These results indicate that the automation system provides a greater accuracy and is suitable for weed cultivation tasks in organic farming. Full article

To address the issues of low trajectory planning efficiency, high motion impact, and poor operational stability in robotic arms during the automatic loading and unloading of aluminum blocks in coating machinery, a time-optimal trajectory optimization method based on a dynamically adaptive Particle Swarm Optimization (PSO) algorithm is proposed. First, the loading and unloading process of aluminum block components is described, followed by a kinematic analysis of the robotic arm in joint space. Then, the “3-5-3” hybrid polynomial interpolation method is used to fit the robotic arm’s motion trajectory and simulate the analysis. Finally, with the robotic arm’s operation time as the objective function, the dynamically adaptive PSO algorithm is applied to optimize the trajectory constructed by hybrid polynomial interpolation, achieving time-optimal trajectory planning for aluminum block handling. The results demonstrate that the proposed method successfully reduces the trajectory planning times for condition 1 and condition 2 from 6 s to 3.59 s and 3.14 s, respectively, improving overall efficiency by 40.2% and 47.7%. This confirms the feasibility of the method and significantly enhances the efficiency of automated loading and unloading tasks for aluminum blocks in coating machinery. The proposed method is highly adaptable and well-suited for real-time trajectory optimization of robotic arms. It can also be broadly applied to other robotic systems and manufacturing processes, enhancing operational efficiency and stability. Full article