Reduce Labor Costs Research Articles

Motion planning and control of an autonomous mobile robot

The application of autonomous mobile robots (AMRs) has gradually become crucial in smart factories due to the advantages of improving production efficiency and reducing labour costs. Motion planning has been a key part of AMR control development. This paper presents motion planning and position tracking control systems of an omnidirectional wheel AMR powered by a hybrid fuel cell and battery power source. First, the kinematical and dynamic models of the AMR are introduced. The navigation system comprises three loops, with the first loop being motor control, the second loop being position tracking control, and a motion planning layer. The position data of the AMR for feedback control is obtained through sensor fusion of data from the inertial measurement unit (IMU) sensor, encoder sensor, and ranging sensor with simultaneous localisation and mapping (SLAM) algorithm. The motion planning is then applied to obtain an optimal path with the shortest distance and collision-free movement. In addition, the tracking algorithm is designed to drive the AMR to follow the optimal path and achieve high accuracy. The experimental results show a 30% improvement in tracking accuracy compared to traditional approaches and 8 hours of continuous working, which is promising for industrial applications, and the results are satisfactory in terms of both accuracy and efficiency requirements.

Comparison of methods for determination the bearing capacity of piles in punched holes with broadening using the results of static load tests

In recent years, due to the increasing of urban construction extent, there has been a tendency to exhaust of territorial reserves, suitable for construction in large cities. Increasingly, it is necessary to use territories with unfavorable soil conditions for the construction of civil and industrial buildings. In such cases, various pile foundations are used, which have significant technical and economic advantages compared to foundations on a natural bases. Among the progressive designs of pile foundations, a special place is occupied by piles in punched holes with broadening (SPSu), which ensure the improvement of the physical and mechanical properties of the foundation during the production of works, as well as the reduction of material, energy and labor costs during their installation. Piles of this type are designed using the developed regulatory framework ISO 36554501-018-2009 "Design and installation of pile foundations and reinforced foundations from packed piles in punched holes". At the same time, the task of improving the quality of work on the design, arrangement, as well as the assessment and prediction of the bearing capacity of piles becomes particularly relevant. This task is solved with a high degree of accuracy by conducting large-scale tests of piles with a static load tests. These tests are carried out in real conditions of the construction site and allow to observe the changes pile`s settlement under load. The article is devoted to solving the problem of increasing the efficiency of assessing the bearing capacity of piles according to static load tests. Using the example of the test results of single SPSu, the authors of the article assess the effectiveness of the existing domestic and foreign methods for determining the bearing capacity of piles. As part of the solution of this issue, the results of the bearing capacity determining according to SP 24.13330.2021 were compared with the data obtained during the use of Davisson, Chin, De Beer, Hansen, Decourt and Eurocode 7 methods. The advantages and disadvantages of this approaches used are noted. Based on the accumulated practical experience of testing the SPSu in various ground conditions, the authors of the article proposed an alternative method of interpreting the "settlement - load" graph.

RESEARCH ON SELF-CROSS-TRANSFORMER MODEL OF POINT CLOUD CHANGE DETECTION

Abstract. With the vigorous development of the urban construction industry, engineering deformation or changes often occur during the construction process. To combat this phenomenon, it is necessary to detect changes in order to detect construction loopholes in time, ensure the integrity of the project and reduce labor costs. Or the inconvenience and injuriousness of the road. In the study of change detection in 3D point clouds, researchers have published various research methods on 3D point clouds. Directly based on but mostly based on traditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are to convert 3D point clouds into DSM, which loses a lot of original information. Although deep learning is used in remote sensing methods, in terms of change detection of 3D point clouds, it is more converted into two-dimensional patches, and neural networks are rarely applied directly. We prefer that the network is given at the level of pixels or points. Variety. Therefore, in this article, our network builds a network for 3D point cloud change detection, and proposes a new module Cross transformer suitable for change detection. Simultaneously simulate tunneling data for change detection, and do test experiments with our network.

A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations

Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0260 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0260 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

DURIAN DISEASE CLASSIFICATION USING TRANSFER LEARNING FOR DISEASE MANAGEMENT SYSTEM

Durian fruit is one of the popular fruits in ASEAN countries, even in European countries, making it a high-potential contributor to economic growth in the agricultural sector. However, durian leaf diseases pose significant challenges in most ASEAN countries, such as Malaysia, Indonesia, the Philippines, and Thailand. Traditionally, the identification of leaf diseases relied on manual visual inspection, a labor-intensive and tedious process. To address this issue, we propose a novel approach for durian leaf disease detection and recognition using vision transformers. We employed well-established deep learning models, VGG-19 and ResNet-9, with carefully tuned hyperparameters including epochs, optimizer, and maximum learning rate. Our results indicate that ResNet-9 achieved an impressive accuracy rate of 99.1% when using the Adam optimizer with a maximum learning rate of 0.001. This breakthrough in automated disease recognition promises to significantly reduce labor costs and time for smallholder farmers, enhancing the sustainability of durian cultivation.

Three-Dimensional Printing of Multifunctional Composites: Fabrication, Applications, and Biodegradability Assessment.

Additive manufacturing, with its wide range of printable materials, and ability to minimize material usage, reduce labor costs, and minimize waste, has sparked a growing enthusiasm among researchers for the production of advanced multifunctional composites. This review evaluates recent reports on polymer composites used in 3D printing, and their printing techniques, with special emphasis on composites containing different types of additives (inorganic and biomass-derived) that support the structure of the prints. Possible applications for additive 3D printing have also been identified. The biodegradation potential of polymeric biocomposites was analyzed and possible pathways for testing in different environments (aqueous, soil, and compost) were identified, including different methods for evaluating the degree of degradation of samples. Guidelines for future research to ensure environmental safety were also identified.

Design and implementation of a wireless communication-based sprinkler irrigation system with seed sowing functionality

This study addresses the critical health risks faced by farmers owing to the use of harmful chemical pesticides in agriculture. The primary objective is to create an effective solution to minimize these risks and reduce the use of pesticides. To achieve this, a smart irrigation system has been implemented by connecting various sensors, such as moisture sensors and thermal imagers through the Internet of Things. These sensors collect vital data on crop moisture levels and thermal images that are securely stored in a cloud-based system. The data collected were subjected to extensive analysis to ensure accurate pesticide use and to identify specific pests affecting crops. In addition, the smart irrigation system includes an Android phone for remote monitoring and pesticide spray detection, thus offering a convenient remote-based operating system for farmers. This innovative system not only proved to be cost-effective but also proved to be significantly more efficient than traditional methods, resulting in reduced labor costs. Importantly, it not only addressed the health risks associated with pesticide use but also led to a significant reduction in overall pesticide use in agriculture. This research provides a comprehensive and effective approach to address the health risks farmers face from harmful pesticides, thereby promoting sustainable and safe farming practices for the future.

Design and validation of an open-sourced automation system for vertical farming

The Modular Automated Crop Array Online System (MACARONS) is a scalable, customisable and open-sourced platform designed for plant care, monitoring, and transportation. It offers specific dosing for individual plants, automated data logging of temperature, humidity, and images, and custom behaviours programmable in Python. Monitoring and control of the system is achieved through a web-interface. The system was validated by autonomously caring for five lettuce plants over a five-week period. This was done indoors under artificial lighting and uncontrolled ambient conditions. The system is estimated to perform the tasks required 30% faster than a human operator and can handle payloads of up to 5 kg with a maximum footprint of 750 mm × 500 mm. The validated system supports 12 payloads and can be easily scaled to accommodate more. The designs are released and meets the requirements of CERN-OSH-W, which includes step-by-step graphical build instructions and can be built at a cost of GBP 2241.72 (USD 2793.82). The system aims to provide cost-effective automation to reduce labour costs and provide precise control of irrigation and nutrients. The current system is limited by the dosing time and the space-use efficiency. We provided future directions and modifications that can be made to address this.

Cost effective maintenance strategy for centrifugal pumps using reliability centred maintenance

The aim of this work is to obtain a maintenance strategy that is cost effective for centrifugal pumps using reliability centred maintenance (RCM). The components of the centrifugal pump used for the study are the shaft, impeller, bearing, mechanical seal, coupling, shaft sleeve, wear-ring casing, suction flange and discharge flange. The motor that drives the pump was also considered. Two basic tools of the RCM strategy were applied. These are the failure modes, effects and criticality analysis (FMECA) and logic tree analysis (LTA). Failure data for a period of 10 years about a centrifugal pump was collected and the FMECA was applied to determine the failure modes, the effects of the failure, the criticality of the components, the mean time between failure (MTBF), the failure rate and the mean time to repair (MTTR). The risk priority numbers of all the components were estimated. The LTA was applied to come up with a new maintenance strategy for each of the components of the pump. The maintenance cost consisting of spare part cost and labour cost for the pump components were estimated for the current maintenance practice as well as the new or proposed maintenance practice. The most critical components observed were the motor, shaft, impeller and the mechanical seal. The motor has the highest MTTR value (58 hrs) while the mechanical seal has the highest failure rate (8.06E-04 per hr). The proposed maintenance strategy leads to reduction in spare part cost in a number of components, with the largest reduction of 19.88% occurring in mechanical seal. The proposed strategy also leads to reduction in labour cost in motor, shaft, impeller and the mechanical seal with the values 33%, 25%, 50% and 25% respectively. The methodology could be applied to other devices.

Plucking Flowers using Soft Robot

Abstract: Soft robotics is a subfield of robotics that concerns the design, control, and fabrication of robots composed of complaint materials, instead of rigid links. In contrast to the rigid-bodied robots built from metals, ceramics, and hard plastics, the compliance of soft robots can improve their safety when working in close contact with humans. The main objective of this project is to pluck flowers using a soft robot. The proposed system is designed to provide gentle manipulation of flowers in a horticultural setting. The soft robot is composed of flexible and deformable materials, such as silicone or elastomer, and is designed to mimic the motion and compliance of human fingers. The system is implemented and tested in a real-world scenario, and the results show that it can effectively pluck flowers without causing damage or injury to the plant. The proposed approach has potential applications in the floriculture industry, where the system can improve efficiency and reduce labour costs, while also minimizing damage to the flowers

Automatic Fruit Plucking Machine Using Deep Learning

Abstract: The Automatic Fruit Plucking Machine project aims to develop an innovative automated system to revolutionize fruit harvesting. By integrating computer vision, robotic arm manipulation, and YOLO deep learning algorithms, the machine can accurately identify and pluck ripe fruits. It reduces labor costs, enhances harvest quality, and improves productivity. The machine's core is a trained machine learning algorithm that combines image processing and deep learning models for real-time fruit detection. Through iterative testing and refinement, the machine's performance continually improves. This project offers a sustainable and efficient solution for fruit producers to meet market demands and reduce manual labor reliance

An improved YOLO algorithm for detecting flowers and fruits on strawberry seedlings

Accurately identifying the flowers and fruits of strawberry seedlings in the greenhouse is the key to automated flower and fruit thinning, which can improve efficiency and reduce labour costs in the cultivation. To address the challenges resulting from the small size, similar colour, and overlapping occlusion of strawberry seedling flowers and fruits, this paper proposes a real-time recognition algorithm (Improved YOLO) for accurately identifying them. Firstly, a Swin Transformer prediction head on the high-resolution feature map of YOLO v7 was constructed to better utilise spatial location information to enhance the detection of small target flowers and fruits, and improve the model's spatial interaction and feature extraction ability in scenes with similar colours and overlapping occlusions. Secondly, the GS-ELAN Optimisation Module for neck of network by GSConv was constructed to suppress shallow noise interference from the high-resolution prediction head and mitigate the increase of parameters resulting from high-resolution prediction heads. The experimental results showed that the Precision(P), Recall(R), and mean Average Precision (mAP) of Improved YOLO are 92.6%, 89.6%, and 92.1%. In the meantime, the Improved YOLO algorithm achieves a frame rate of 45 f/s, satisfying the real-time detection requirements. It is 3.2%, 2.7%, and 4.6% higher than the precision, recall, and mAP of YOLOv7, respectively. The accuracy of this model for detecting flowers and fruits was 93.9% and 91.3%, the recall was 93% and 86.3%, and the average precision was 94.7% and 89.5%, respectively. The Improved YOLO algorithm has a high level of robustness and real-time detection performance, allowing it to quickly and accurately identify the flowers and fruits of strawberry seedlings and provides effective support for the automated management of flower and fruit thinning of strawberry seedlings in greenhouse environments.

Finding the right one: Decision support for selecting cost-efficient order picking solutions

Enabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity.

Life cycle management of the ventilation system of a construction object

Introduction. In the article, the author considers a mechanical ventilation system as a complex technical system that can be effectively controlled based on a model of its life cycle. Like any engineering system (or product, product, project), a ventilation system has its own life cycle, consisting of a set of successive stages. Each stage is characterized by its own types of work and final results that require management decisions.Materials and Methods. The paper uses the method of analyzing the life cycle of complex technical systems, methods of collecting and processing statistical data, as well as methods of systematic and comparative analysis, generalization of scientific and practical results.Results. In the course of the conducted research, the author applied the "life cycle management" approach to ventilation systems and achieved the goal of developing a model of their life cycle, which includes all stages of system development from conception to disposal. Possible ways of managing the life cycle of ventilation systems from the perspective of continuous interconnection of processes are determined.Discussion and Conclusion. Effective life cycle management of a mechanical ventilation system can be implemented through the development of a software product capable of modeling processes and system elements even at the first stages of the life cycle. The software product should eliminate the existing problem of inconsistencies at different stages of work, store information about the object and provide access to it for each participant in the process. Solving the problem of creating a software product will ensure effective management of the entire life cycle of the ventilation system, reduce labor costs and inconsistencies and ensure compliance with modern requirements for operational reliability and energy efficiency of a vital engineering system.

Computing the characteristics of defects in wooden structures using image processing and CNN

Wood, a time-honored construction material prized for its exceptional properties, has been in use for millennia. Its enduring popularity is attributed to its remarkable strength, aesthetic appeal, and favorable environmental footprint. However, wooden structures are susceptible to various defects and imperfections that pose threats to their structural integrity, durability, and safety. These issues encompass knots, cracks, warping, twisting, decay, insect infestations, and more, all of which, if left unaddressed, can culminate in structural failures. Thus, a comprehensive strategy involving inspection, maintenance, and remediation is indispensable for safeguarding wooden structures. Traditional manual inspections, while effective, are characterized by their resource-intensive nature, entailing significant time and cost investments. This study presents a pioneering approach that leverages Convolutional Neural Networks (CNNs) and Image Processing techniques to revolutionize the assessment of damage in wooden structures using digital imagery. Initially, CNNs are employed to categorize images into three fundamental classes: cracks, knots, and undamaged sections. Subsequently, Image Processing techniques are harnessed to compute precise characteristics of these defects, including parameters such as crack length, width, angle, and the extent of the defective area within knots. The Inception-ResNet-V2 pre-trained model is utilized, fine-tuned and validated with a robust dataset comprising 9000 wooden defect images, evenly distributed across the three aforementioned categories. A prudent division allocates 70% of the dataset for model training, with the remaining 30% reserved for validation. Following successful training, the model demonstrates an impressive overall accuracy of 92% when classifying an independent test set comprising 100 new images. To illustrate the model's performance, two images from each damage category are selected and tested to compute the characteristics of the defects. The quantification error for crack angle is only 0.15%, while it is 0.99% for crack length, and 2% for crack width, demonstrating the high performance of the model. The practical implications of this work are profound. By automating defect assessment in wooden structures, our approach offers significant advantages to industry professionals. It expedites inspections, reduces labor costs, and enhances the accuracy of defect quantification.

Effect of labour costs on wood harvesting costs and timber provision

Efficient forest operations are essential for forest enterprises, who provide wood and numerous ecosystem services for the society. Important factors influencing the efficiency of forest operations, and thus the harvesting costs, are the level of mechanization, the harvesting method applied, the forest road network, and the training of the labourers. The cost of labour, i.e. salaries, is another important driver of harvesting costs. However, its effect on and relative importance for overall harvesting costs is poorly described in the scientific literature. Thus, this study aims to analyse the influence of labour costs in more detail, especially on the country-wide wood harvesting potentials. In addition, we aimed to cross-compare the effect of labour costs with the effect of switching to a more efficient harvesting method. For this purpose, we calculated timber harvesting costs with varying salaries for all sample plots of the Swiss National Forest Inventory (NFI) for both, the currently applied harvesting method recorded in the NFI, and the potential best suitable harvesting method. A 1% change in labour costs affects harvesting costs by 0.33–0.77%, depending on the harvesting method applied. The influence is larger for systems that involve a large share of motor-manual work and for cable-based methods. Changing labour costs by ± 30% affects the number of plots for which timber harvesting is economically feasible, by 5 to 15 percent points. The effect of switching from the current to the best suitable harvesting method is comparable to that of reducing labour costs by 15–30%. These results indicate that the efficiency of wood harvesting can be increased with further mechanization and does not require cutting salaries of forestry personnel. In that, our results may inform forestry planning and policy making at regional to national level.

Visual Servo Kinematic Control of Delta Robot using YOLOv5 Algorithm

Industrial delta robots with motion-and vision-base controllers, providing high-precision and fast/flexible motion, normally come with high cost and complex software. Low-cost delta robots with vision solution could be beneficial to vast industries to increase productivity and to reduce labor cost. A delta robot using a low-cost motion controller and an open-source vision system is developed to accomplish real-time visual servoing with high motion accuracy. In the low-cost motion controller, three parallel links’ upper-arm angles, computed from inverse kinematics for a given desired target position by a high-level computer, are used as reference position commands for three AC-motor drives. A low-level Arduino microcontroller is employed to convert these links’ angles to high-frequency pulses and on-off signals for synchronously controlling three motor angles and direction. Experimental results of a point-to-point motion tracking exhibit high-precision repeatability. Synchronous pulse generation from Arduino microcontroller and structural misalignments of parallel links are major challenges for achieving high motion accuracy. For the vision-based system, the YOLOv5 algorithm is implemented along with a Python GUI Application. Then, the visual-servo performance is evaluated on localization accuracy and recognition rate of 3-color objects. However, a partial object occlusion can reduce the visual classification rate. A sorting task of 4-category medicine boxes demonstrate a high-speed pick-and-place operation using the low-cost visual-servo system of this delta robot. Therefore, integration of low-cost visual servoing with this delta robot can revolutionize various industries, like automobile, pharmaceutical, and food sectors, in separating, sorting and packing applications.

Benefit evaluation analysis of new energy power consumption information collection with basic steady-state harmonic characteristics

Abstract In this paper, firstly, the load monitoring characteristics of new energy power consumption are studied and analyzed in terms of both steady-state and transient characteristics, and suitable characteristic values are selected for different types as the basis of load decomposition. A comparative analysis of various types of load imprints is conducted to highlight the advantages of steady-state harmonic features as load monitoring feature data. Then, an evaluation model of the demand response potential of electricity information collection is established based on a multi-objective optimization algorithm, and technical and economic analysis of electricity information collection is carried out to evaluate the response potential of power companies collecting customer data. Finally, the situation and benefits of collecting electricity consumption information for new energy power are analyzed. The daily maximum load moments of typical users are all cut by more than 10%, the peak load portion cut by more than 97% is also more obvious, and the maximum moments of valley filling response are all more than 11%. D Power saved a total of 3.759 billion yuan in labor costs by reducing labor costs by 400 million yuan after adopting electricity consumption information collection. The indices of excellent and qualified management benefit and social benefit evaluation of electricity consumption information collection are above 0.8. Overall, the benefits of electricity consumption information collection for new energy power with basic steady-state harmonic characteristics are significant and effective.

Size matters: Microherbivores make a big impact in coral aquaculture

Reef restoration activities and the ornamental trade are increasing the demand for sexually propagated corals. One challenge faced in scaling up the aquaculture production of corals is high mortality as a result of fouling organisms overgrowing coral spat, with manual removal of algae and other fouling organisms being costly and time consuming. Here we test the use of microherbivore grazers as a potential biocontrol method for fouling in coral aquaculture and compare their effectiveness to manual cleaning by an aquarist. Recruits of six coral species (Acropora millepora, Acropora kenti, Goniastrea retiformis, Porites lobata, Dipsastraea speciosa and Lobophyllia corymbosa) were reared for 112 days with aquarist cleaning, or co-cultured with gastropods (Calthalotia strigata or Turbo haynesi), sea urchins (Tripneustes gratilla or Echinomentra mathaei), the hermit crab Clibanarius cf. taeniatus or under a control treatment with no grazers. Corals grown in the aquarist cleaning treatment displayed high survival and growth, though similar responses were observed for most coral species grown with C. strigata or T. gratilla, likely due to minimal damage via overgrazing and the promotion of relatively short turf algal communities in these treatments. However, effort required, measured as average cleaning time, was 2–3 times greater in the aquarist treatment compared to C. strigata or T. gratilla treatments. Survival of coral recruits housed with C. cf. taeniatus, E. mathaei or T. haynesi were variable, likely due to the dominance of long, filamentous turf algae in tanks with E. mathaei, and physical disturbance to recruits by C. cf. taeniatus and T. haynesi. Our results demonstrate microherbivores have potential for application in aquaculture to promote production, while also reducing labour costs.

Magpie cleaning device for cleaning the pre-chamber of the pumping station from sediment

The purpose of the work is to develop, test and evaluate the efficiency of the water treatment device at the water intake of the pumping station. The article provides an analysis of the existing designs of water intakes at pumping stations of irrigation systems of the Saratov region. A brief description of the device and operation of the developed conveyor-type water treatment device mounted on the water intake of the pumping station No. 4 of the Engels OS is given. Studies have established the species composition and mass of sediment entering the grid during the irrigation period. When filling the channel (May 20), a large mass of sediment in the form of branches, stems and leaves of plants was observed in the ante-chamber. At the first watering, the mass of the removed dry sediment is small ? 2-3 kg. With the second watering, the weight of the sediment increases to 14 kg. The maximum mass of sediment was removed from the pre-chamber during the third and fourth watering (July, August), and reached 17, 21 and 24 kg. In addition to branches, stems and leaves, a large amount of algae, mud, duckweed and a small amount of household garbage were observed in it. The cleaning time of the ante-chamber in front of the conveyor varied from 10 to 60 minutes. The developed water treatment device showed reliable operation, reduced labor costs for cleaning the ante-chamber and ensured the uninterrupted operation of the pumping station during the irrigation period. Proposals for further improvement of the water purification device have been developed.