Spraying Robot Research Articles

Designing and prototyping of a reconfigurable segmented fan concrete shell as a flooring system

A significant portion of the environmental impact of a building’s superstructure lies in its structural flooring. By leveraging funicular forms such as thin concrete shells, a materially and carbon-efficient alternative to bending-active flooring systems can be attained. In addition, through segmentation and the use of dry jointed interfaces, a segmented concrete shell allows for ease of disassembly compatible with circular economy principles for the built environment. This paper presents a novel segmented concrete shell flooring system that leverages the symmetry of revolution of the classical fan vault form to facilitate future design flexibility through increased reconfigurability. The design and form-finding of the segmented fan concrete shell are detailed through the use of an evolutionary algorithm and finite element analysis. Quarter-scale prototypes were digitally fabricated using a robotic concrete spraying process which were then assembled and tested to assess its structural potential, evaluate the limitations, and identify areas of future work. An embodied carbon analysis demonstrates that the system provides a mass and embodied carbon saving compared to conventional flooring systems while adding approximately a 20% embodied carbon premium over a comparable non-reconfigurable segmented shell flooring system. Rephrased, the proposed system provides a positive embodied carbon saving if enabling design flexibility through reconfiguration increases the life-span of the system by at least 20%. Through this work, it is shown that a segmented fan concrete shell presents a viable lightweight and carbon-efficient flooring system which has the potential to become a sustainable alternative that enables disassembly, reuse, and even reconfigurability for circular construction provided further research and development to address its current limitations for adoption in industry practices.

Model Predictive Controller Design for a Pesticide Spraying Robot

Model Predictive Controller Design for a Pesticide Spraying Robot

Evaluation of 3D robotic spray parameters on the performance of the developed sensing functional cementitious coating

ABSTRACT This study explores the innovative use of 3D robotic spray for applying self-sensing cementitious coating, emphasising its potential for multifunctional smart concrete applications. The effects of spray parameters on the performance of self-sensing cementitious composites were investigated by systematically examining the impact of nozzle travel speed, air injection pressure, nozzle standoff distance, and spray direction on the self-sensing coating. In-depth analyses were conducted on mechanical strength, coating adhesion properties, and sensing performance. Micro-CT was performed to investigate the coating's inherent porosity. This study evaluated the sensing performance of the coatings by analysing their piezoresistive behaviour under cyclic compression and bending. Furthermore, it demonstrated the coating's capacity for real-time structural health monitoring by evaluating its performance under compressive and bending stresses until failure. This research underscores the significant impact of spray parameters on optimising the sensing capabilities of the self-sensing spray coating, highlighting its potential for advanced real-time structural health monitoring.

Enhancing crop protection through smart autonomous pesticide spray bot in sustainable agriculture

Abstract Pesticide spraying is a common practice employed to safeguard crops against harmful pest infestations and mitigate crop losses. However, the conventional use of handheld pesticide sprayers by the farmers raises concern regarding adverse health effects, including fatalities, and the environmental damage caused by excessive pesticide dispersion. Present robotic spraying solutions over and over again lack the precision and adaptability essential to report these problems effectively. This work covers the design of a smart autonomous pesticide spray bot that combines an advanced robotic architecture with a deep learning-based pest detection module, driven by the desire to improve both environmental sustainability and human safety. The bot autonomously navigates entire fields, utilizing a Convolutional Neural Network (CNN) trained on a diverse dataset to detect pests on plant leaves with high accuracy. Upon detection, the bot promptly activates its spraying management system, it has an adaptive spraying mechanism that precisely applies the amount of pesticide required to target the pests that have been identified. Evaluation of the system’s performance in a cotton field yielded significant results, including a robot speed of 0.25 m s−1, a pest detection accuracy of 97%, an average droplet size of 60 microns, a spray nozzle pressure of 7 bar, and a pesticide flow rate of 25 ml/s. The system raises issues with data dependency and expenses even if it has several benefits in terms of environmental conservation and public health. However, this system stands out as a major development in automated agriculture technology since it combines deep learning with a strong architectural design.

Human-Centered Robotic System for Agricultural Applications: Design, Development, and Field Evaluation

This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture.

Agent-based modeling system for real-time characterization of film thickness in digital twin coating

In the automated spraying workshop of ship segments, poor visibility caused by dust and fog hampers the effectiveness of the video monitoring system. The traditional method used during the automated spraying process fails to provide real-time measurement of film thickness and prediction of spraying quality, resulting in uneven film thickness and high rework rates. Robotic spraying technology also faces challenges, such as the time-consuming and labor-intensive process of building and computing a highly reliable Computational Fluid Dynamics (CFD) based spraying film-forming simulation model, which cannot be reflected in real-time during the spraying process. The digital twin characterization method, based on the agent model, offers a solution to these limitations and challenges, providing new ideas and approaches for improving and optimizing the spray film formation model. This paper presents the development of a real-time characterization system for sprayed film thickness based on digital twin technology. The spraying film thickness under different working conditions is obtained using Ansys-Fluent simulation software by varying the gun height and gun speed. The predictive model for spraying film thickness is then established using the obtained simulation data and the corresponding gun height and gun speed, employing the surrogate modeling method (BP neural network). The real-time characterization system for spraying film thickness is developed by combining C# and Unity3D. Finally, in order to ensure the accuracy of the predictive model, a spraying experimental platform is built to verify the accuracy of the predictive model.

Deep learning guided variable rate robotic sprayer prototype

This paper presents the development of a robotic sprayer that combines artificial intelligence with robotics for optimal spray application on citrus nursery plants grown in an indoor environment. The robotic platform is integrated with an embedded firmware of MobileNetV2 model to identify and classify the plant samples with a classification accuracy of 100 % which is used to dispense variable rate spraying of pesticide based on the health status of the plant foliage. The disease detection model was developed through the edge impulse platform and deployed on Raspberry Pi 4. The robot navigates through an array of plants, stops beside each plant, and captures an image of the citrus plants. It feeds the image into the deployed embedded model to generate a disease inference that informs the variable rate application of spray during real-time actuation. To test the spraying performance of the prototype within the growing environment, water sensitive cards were placed in each plant's canopy. After spraying, the samples of water sensitive cards were collected and quantified using a smart spray app to determine the classification accuracy as well as the extent of spray coverage on the citrus samples. The robot spray coverage results show an average spray coverage of 87 % on lemon foliage when compared with 67 % for navel orange, during the spray performance test of the robot.

Design and systematic evaluation of an under-canopy robotic spray system for row crops

Despite having made much improvement in sensing, automation, and control, the current broadcast spraying system has several drawbacks, such as uneven coverage, excessive chemical use, and deviation from recommended dosage. Typically, farmers use large self-propelled sprayers to spray the entire field without knowledge of spatial pest severity, potentially resulting in an unintentional application. However, application errors and the extent of chemical use can be optimized by utilizing an intelligent site-specific decision-based sprayer to control pests more efficiently. Hence, the initial project goal was to design a robotic liquid application system for row crops (e.g., sorghum and corn) and validate sprayer system performance. The critical design considerations for the spray application system were modularity; the ability to be mounted on an autonomous platform to go within 76.2-cm spaced crops; spray on either side of the crop row using spray booms; onboard hardware and software for control and data acquisition; and record as-applied data. A system with desired design requirements was built and individual sub-systems were tested under simulated lab scenarios to quantify the response time and accuracy of the spray system. The results showed that the sprayer could maintain an average system pressure within ±5% of the target under different duty cycles for each of the six nozzles. At 40% duty cycle, the nozzle pressure settling time at an error margin of ±5% from the mean was 13 ms, 20 ms, and 19 ms, for one, three, and six nozzles, respectively. Also, no substantial pressure difference was observed between nozzles installed at different heights in two different booms. Therefore, this application system could be a viable solution for autonomous platforms to site-specifically apply pesticides only on critically infested plants, has the potential to decrease the overall input costs on chemicals and reduce the negative environmental impacts.

Agricultural Pesticide Spraying Robot

Abstract: The goal of this project is to develop an intelligent spraying robot that will reduce the usage of pesticides and harm to human health, protecting farmers and requiring less labor. Complete route planning and navigation systems, driving control, a spraying mechanism, system construction, obstacle avoidance, and the integration of several sensor modules will all be features of the robot. The design of the spray robot will include simulations and analysis for sensor integration, obstacle avoidance, and spraying. In order to achieve strong stability and dependability, it is utilized not only to track motion and monitor orientation but also to adjust for path errors. In the interim, the spraying system will be enhanced with automated sprays that adjust based on the target in order to remove leaks and avoid repeated spraying. The pesticide spraying strategy that this study suggests will assist farmers in the agricultural sector.

PESTI GUARD: FARMER-FRIENDLY PESTICIDE ROBOT

Agriculture is the primary source of revenue for India's population, which accounts for nearly 60% of the country's total. Farmers work in their fields to cultivate various crops based on the environment and resources available. Farmers must use large quantities of pesticides to increase food production in order to meet such high food demand for such a large population. Traditional manual pesticide spraying operations is full of direct exposure to the pesticide liquid work environment, great harm to human body and when this pesticide may come into contact with the farmer during spraying, which may trigger skin cancer and asthma illnesses. Increased pesticide spraying can impact consumer health as it enters the food chain. The widespread use of pesticides in agriculture presents significant health risks to both farmers and consumers. The pesticide spraying robot is equipped with state-of-the-art sensors to record real-time temperature and humidity levels, providing crucial environmental data for optimized pesticide application. Farmers can remotely control the robot using a user-friendly interface, instructing it to perform specific spraying tasks while maintaining a safe distance from potentially hazardous chemicals. To address this challenge, this paper proposes a Pesticide Spraying Robot designed to minimize human contact during operation while prioritizing human health through advanced environmental monitoring and remote-control capabilities.

DESIGN AND FABRICATION OF SMART AGRICULTURE SPRAYER

The aim of this project is to create an intelligent spraying robot that will decrease pesticide use and human health damage, allowing farmers to be protected and labour intensity can be reduced. More than 60 percent of the population in India do agriculture as the primary sector occupation. At present, due to increase in shortage of labour, interest has raised for the development of the autonomous vehicles like robots in the agriculture field. A system called “Design and Fabrication of Smart Agricultural Sprayer” has been designed to minimize the labour cost of farmers in addition to increasing the speed and accuracy of the work. The Proposed system is designed with the multipurpose autonomous agricultural Smart vehicle which can be controlled through ESP module, for spraying pesticide on the plants but not in free space, The project was tested on the field, The robot is successfully able to move in all the direction. Pesticide spraying unit is capable of spraying pesticide only on the plant not in the free space.

Navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet

AbstractThe continuous and close combination of artificial intelligence technology and agriculture promotes the rapid development of smart agriculture, among which the agricultural robot navigation line recognition algorithm based on deep learning has achieved great success in detection accuracy and detection speed. However, there are still many problems, such as the large size of the algorithm is difficult to deploy in hardware equipment, and the accuracy and speed of crop row detection in real farmland environment are low. To solve the above problems, this paper proposed a navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet. First, the Convolution (Conv) module and C2f module of YOLOv8s network are replaced with Depthwise Convolution (DWConv) module and PP‐LCNet module respectively to reduce the parameters (Params) and giga floating‐point operations per second of the network, so as to achieve the purpose of network lightweight. Second, to reduce the precision loss caused by network lightweight, the spatial pyramid pooling fast module in the backbone network is changed to atrous spatial pyramid pooling faster module to improve the accuracy of network feature extraction. Meanwhile, normalization‐based attention module is introduced into the network to improve the network's attention to corn plants. Then the corn plant was located by using the midpoint of the corn plant detection box. Finally, the least square method is used to extract the corn crop row line, and the middle line of the corn crop row line is the navigation line of the corn spraying robot. From the experimental results, it can be seen that the navigation line extraction algorithm proposed in this paper ensures both the real‐time and accuracy of the navigation line extraction of the corn spraying robot, which contributes to the development of the visual navigation technology of agricultural robots.

A Review Paper on Paint Spraying Robot

Abstract: Painting interior walls is a frequent construction task that takes a lot of time and work. Robotic painting was introduced to replace human manual activity, improving accuracy, efficiency, and lowering costs. In this study, We present an independent robot that paints walls. robot that uses a cascade lift mechanism to enable it to use a paint sprayer to paint a room's interior walls. The paint sprayer can reach the necessary heights With the assistance of of this mechanism for cascading lifts mechanism. With two degrees of freedom (DOF), The robot moves fluidly in each of the six directions. thanks to the DC powered mecanum wheels mounted down to its foundation. Ultrasonic sensors are used by the robot to measure distance, make adjustments to the walls, and determine if the mister has reached the wall's summit. The robot's mecanum wheels, ultrasonic sensors, and other components are all managed by the master controller. The AC power supply powers the entire system Color is sprayed on to manage the amount of color; sprayers are employed for this purpose. Sprayers need to split liquid into droplets that are the right size, distribute them evenly across the surface, and manage the volume of liquid to prevent overapplication. One major issue thataffects workers everywhere is disease control

Embedded System – Based Pesticides Spraying Robot

The agriculture sector is undergoing a transformation with the integration of robotics and automation. One notable development is the emergence of autonomous pesticide spraying robots, designed to enhance precision, efficiency, and sustainability in crop protection practices. This abstract explores the technological advancements, operational mechanisms, and potential implications of these innovative robots. Autonomous pesticide spraying robots leverage cutting-edge technologies such as artificial intelligence, machine learning, and advanced sensors to navigate fields, identify crop areas, and precisely apply pesticides. Equipped with GPS, LiDAR, and cameras, these robots can map fields, detect obstacles, and optimize spraying patterns in real-time, reducing pesticide usage while ensuring effective pest control. Furthermore, the integration of data analytics enables continuous improvement in spraying strategies, tailored to specific crop types, environmental conditions, and pest pressures. The deployment of autonomous spraying robots offers several benefits to farmers, including increased operational efficiency, reduced labor costs, and minimized environmental impact. By eliminating the need for human operators and optimizing pesticide application, these robots contribute to safer working conditions and reduced chemical exposure risks. Moreover, their ability to operate autonomously for extended periods enhances productivity and scalability in large-scale agricultural operations. Additionally, there is a need for comprehensive training and education to enable farmers to effectively integrate these technologies into their existing practices while maximizing their benefits. modern agriculture, offering precision, efficiency, and sustainability in crop protection. As technology continues to evolve and adoption grows, collaboration among stakeholders is essential to address challenges and unlock the full potential of these innovative solutions for the benefit of farmers, consumers, and the environment.

Research on the trajectory planning and global optimization strategy of cold spraying technique for complex products coating preparation

PurposeThis paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.Design/methodology/approachThe complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.FindingsThe simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.Originality/valueThe proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

Development of robotic sprayable self-sensing cementitious material for smart structural health monitoring

Self-sensing cementitious materials are critical to smart structural health monitoring with the piezoresistivity as an indicator for internal stress and cracks in the structure, which have been frequently utilized as functional coatings in engineering practices. To facilitate the automatic deployment of self-sensing cementitious materials, the compatibility with robotic spraying has been systematically investigated in this study. Pre- and post-spraying evaluations have been conducted for the mixtures with carbon fibers at different dosages, including rheology, tackiness, compressive strengths, and electromechanical responses. On this basis, the addition of 0.4 vol.% carbon fiber was confirmed as the optimal dosage with both competent spraying performance and self-sensing function in the corresponding mixture. Furthermore, the porosity characteristics and fiber agglomeration of the designed mixtures were quantitatively assessed by micro computed tomography (micro-CT), which suggested their relationship to the mechanical and self-sensing properties. Apart from the structural application potential of the developed self-sensing mixture, this study also provides detailed guidance for the optimization of other value-added robotic sprayable cementitious materials.

Experimental investigation on natural fiber material for pesticide spraying mobile robot structure

Natural fibers has recently become one of the most crucial material available in the environment. Natural fiber has gained popularity due to its light weight, greater specific stiffness, low cost, easily available, and longer fatigue life. It has high-specific properties such as impact resistance, low density, flexibility, biodegradability, and economy are exhibited by these natural fibers. The other material used in robot include issues like temperature sensitivity in plastic, weight, and corrosion in iron metal, and composites. The main aim of this paper is to test how natural fiber material is best suitable to use for building a chassis of pesticide spraying robot. This work focuses on test specimens made by hand layup technique from a mixture of natural fibers from sisal wood and epoxy resin production procedure. Tests conducted on a 50/50 fraction of these materials show encouraging results 40.13 Mpa for tensile strength, 24.26 N/mm2 for compression strength, and 0.0384 for the Izod Impact test result. Additionally, a 30–70 percentage shows an Izod Impact test result of 0.0239, a tensile strength of 65.66 Mpa, and a compression strength of 21.86 N/mm2. The result showed that natural fiber is the most appropriate material used in pesticide spraying robot structure. Sisal natural fiber material has potential for use in a variety of robotic applications, especially in fields that require materials that are ecologically benign, biodegradable, and lightweight. By including more possible natural fibers, altering the fiber's composition and orientation, and analyzing its mechanical and machining properties, the experiments can be expanded.

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

Autonomous navigation of orchard spraying robots can effectively improve operational efficiency and reduce workload. The conventional robot navigation solutions based on the Global Navigation Satellite System (GNSS) are susceptible to signal loss in orchards due to interference from the canopy of fruit trees. In this paper, an autonomous navigation scheme for orchard spraying robots based on multi-sensor devices and the three-dimensional (3D) LiDAR Simultaneous Localization and Mapping (SLAM) technology. The initial step of the scheme involved the construction of a 3D point cloud map of the orchard using the 3D LiDAR SLAM algorithm. Subsequently, a designed point cloud map transformation algorithm was used to convert the 3D point cloud map of the orchard into a two-dimensional (2D) grid map containing key feature information of the orchard. To enhance the mapping accuracy of the orchard point cloud, a point cloud registration algorithm combining Iterative Closet Point (ICP) and Normal Distributions Transform (NDT) was proposed. This algorithm initially utilized the NDT algorithm for coarse point cloud registration to obtain transformation parameters. Then, the ICP algorithm was applied in conjunction with the obtained parameters for fine registration. Regarding obstacle avoidance, a cooperative obstacle avoidance algorithm based on the Robot Operating System (ROS) multithreaded communication mechanism was introduced. Experimental trials conducted in a standardized spindle-shaped peach orchard validated the successful completion of the SLAM mapping trajectory, which was achieved through the utilization of the proposed NDT_ICP point cloud registration algorithm. The average absolute pose error between the mapped trajectory and the reference trajectory was 1.173 m, with a standard deviation of 0.498 m. The robot's positioning and navigation errors increased with higher speeds, with the largest positioning deviation observed at 1.2 m/s speed, with a maximum lateral positioning error of 7.04 cm. The robot's average lateral navigation error did not exceed 16 cm, and the average heading deviation was less than 8° at different speeds of 0.4, 0.8 and 1.2 m/s. Both the positioning and navigation accuracies of the robot were sufficient to meet the autonomous operational requirements of orchard spraying robots.

Optimization Design of a Recovery System for an Automatic Spray Robot and the Simulation of VOC Recovery

A recovery system for an automatic spraying robot to conduct the spraying operation outdoors for ships is designed in this paper, which addresses the pollution problem of volatile organic compounds (VOCs) by employing the vacuum recovery method. The recovery system consists of the recovery hood, nozzle, and vacuum tubes. The recovery hood is the critical part of the recovery system and is designed with internal and external cavities, as well as four vacuum tubes for recycling VOCs. Based on the computational fluid dynamics (CFD) method, simulation in the time domain of the gas–liquid interaction, droplet evaporation, and wall impingement is conducted. To identify the better recovery performance, three vacuum recovery-hood schemes are designed, and their performance is compared. The numerical results show that the distance between the vacuum tubes and the intake gap has a significant impact on the VOCs’ recovery effect. One of the main reasons for the escape of VOCs is that the swirling airflows in the baffle plane act as vortices which may capture VOCs, causing the accumulation of VOCs beyond the capacity of the external cavity. Dividing the external cavity into four chambers with deflectors (with each chamber equipped with one vacuum tube only) can significantly reduce the leakage rate of the recovery system. The recovery system provides a theoretical solution for implementing the prevention and control of VOCs in shipyards as soon as possible.

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions.