Design and technological development of robotic platforms for agricultural plant care

Background. The modular robotic platform is implemented using the "robot-constructor" principle. The basic platform includes standardized interfaces for connecting various modules – specialized chassis for different types of surfaces, manipulators for cargo handling, and sensor systems for navigation and environmental monitoring. This architecture allows the robotic platform to be quickly adapted to specific customer needs without the need to develop a completely new solution. The platform demonstrates particular practical value in agro-ecological monitoring, where the modular architecture allows for the rapid adaptation of sensor equipment for analyzing key soil parameters. Purpose. To present the architectural and functional design of a modular robotic platform implementing the "robot-constructor" principle and to justify its effectiveness as a basis for creating adaptive ground systems within the national aerospace infrastructure. Materials and methods. The development of a modular self-propelled robotic platform was carried out within the framework of system engineering: conceptual design → synthesis of architecture → selection of components → integration of subsystems → verification on a physical layout. The design is based on a lightweight and rigid metal frame that allows quick replacement of modules (chassis, manipulators, sensors). For work in the agricultural sector, it is possible to switch from a wheeled to a tracked base. Localization and orientation are implemented using visual odometry and simplified SLAM (ORB-SLAM2 light) for building 2D maps. Motion control is a multi-contour PID controller: the external contour corrects the deviation from the trajectory according to the video (P+D), the internal one stabilizes the speed according to the encoder data (I-component). The software platform is ROS 2 Humble (Python 3.10). Key nodes: - vision_node – marker recognition (OpenCV + TensorFlow Lite); - navigation_node – route construction and correction (RRT); - control_node – engine control with adaptive PID adjustment depending on the weight of the cargo; - telemetry_node – data export to JSON/CSV and integration with ERP/MES via REST API. A sensor module is used to monitor the soil: multispectral cameras, humidity, temperature, pH, nutrient sensors, and a sampling device. Results and conclusion. During the project, a modular self-propelled robotic platform was developed and physically prototyped, functioning as a universal ground component within the domestic Aeronet ecosystem. A unified mechanical and electrical platform with standardized connection interfaces (mechanical – quick-release dovetail mounts; electrical – GX16-4P industrial connectors; software – ROS 2-compatible topics), ensuring the modularity of the chassis, manipulators, sensor complexes, and actuators. A visual navigation system has been developed and tested. Based on OpenCV and fine-tuned YOLOv5, an algorithm for recognizing color lines, QR codes, and natural landmarks has been implemented. The platform has been integrated into the educational process at Melitopol State University in four areas of training. The study confirmed the fundamental feasibility and high efficiency of the modular robotic platform as a tool for converging the Aeronet and Technet NTI roadmaps. The developed solution successfully combines the characteristics of technological sovereignty (domestic component base, open-source stack, rejection of dependent technologies), economic affordability, and functional flexibility. The practical significance of the project is due to its dual purpose: 1) as an import-substituting industrial solution for the automation of intra-plant logistics at small and medium-sized enterprises; 2) as a multifunctional educational and research platform that forms a personnel reserve in the field of robotics, AI, and digital manufacturing. Sponsorship information. This study was carried out within the framework of the Russian Science Foundation project No. 25-76-10024 “Monitoring the extent of soil degradation and contamination by heavy metals as a result of military operations in the Zaporizhzhia region”. EDN: SWZMIM

This paper presents the design and experimental validation of a modular educational robot platform. The development of a novel modular unmanned ground vehicle (UGV) platform is based on open-source system architecture featuring carefully considered modules and is specifically tailored for application in STEM education. The proposed platform is comprehensive in terms of motion planning and modelling. Mathematical description of the non-holonomic and holonomic configuration of the robot is given, and a derived model is implemented in a software package in order to perform simulations and experiments. The design process resulted in a 3D printed novel brick-based robot chassis, offering advantages such as low manufacturing costs, easy repair and maintenance, and seamless expandability with additional modules. The proposed modular platform underwent validation across three robot configurations and three different control ecosystems.

The rapid evolution of herbicide‐resistant weed species has revitalized research in nonchemical methods for weed destruction. Robots with vision‐based capabilities for online weed detection and classification are a key enabling factor for the specialized treatment of individual weed species. This paper describes the design, development, and testing of a modular robotic platform with a heterogeneous weeding array for agriculture. Starting from requirements derived from farmer insights, technical specifications are put forward. A design of a robotic platform is conducted based on the required technical specifications, and a prototype is manufactured and tested. The second part of the paper focuses on the weeding mechanism attached to the robotic platform. This includes aspects of vision for weed detection and classification, as well as the design of a weeding array that combines chemical and mechanical methods for weed destruction. Field trials of the weed detection and classification system show an accuracy of 92.3% across a range of weed species, while the heterogeneous weed management system is able to selectively apply a mechanical or chemical control method based on the species of weed. Together, the robotic platform and weeding array demonstrate the potential for robotic plant‐species–specific weed management enabled by the vision‐based online detection and classification algorithms.

Preliminary results of tests facing with the controlled retting of hemp

Design in Mainland China

With the outbreak of novel coronavirus pneumonia worldwide, a kind of epidemic prevention robot is urgently needed in society. The intelligent temperature measurement robot system mainly includes the robot’s high stability multi-attitude omnidirectional movement module, robot path planning and autonomous navigation module, robot environment information acquisition and recognition module, robot remote control module, and robot system integration. According to the design principle of the wheeled robot, high stability and multi attitude omni-directional mobile platform of temperature measuring robot are constructed; path planning and autonomous navigation of robot are realized based on sensor data fusion; temperature detection and remote care function are realized by thermal imaging sensor system by capturing the thermal radiation emitted by the human body, and based on open modular control system architecture. Based on the existing research foundation, the intelligent temperature measurement robot system is established by integrating the robot multi attitude omni-directional mobile system, robot path planning, and autonomous navigation system, robot environment information acquisition and recognition system, and robot remote control system.

KRISO (Korea Research Institute of Ship & Ocean Engineering) started a project to develop the core algorithms for autonomous intervention using an underwater robot in 2017. This paper introduces the development of the robot platform for the core algorithms, which is an ROV (Remotely Operated Vehicle) type with one 7-function manipulator. Before the detailed design of the robot platform, the 7E-MINI arm of the ECA Group was selected as the manipulator. It is an electrical type, with a weight of 51 kg in air (30 kg in water) and a full reach of 1.4 m. To design a platform with a small size and light weight to fit in a water tank, the medium-size manipulator was placed on the center of platform, and the structural analysis of the body frame was conducted by ABAQUS. The robot had an IMU (Inertial Measurement Unit), a DVL (Doppler Velocity Log), and a depth sensor for measuring the underwater position and attitude. To control the robot motion, eight thrusters were installed, four for vertical and the rest for horizontal motion. The operation system was composed of an on-board control station and operation S/W. The former included devices such as a 300 VDC power supplier, Fiber-Optic (F/O) to Ethernet communication converter, and main control PC. The latter was developed using an ROS (Robot Operation System) based on Linux. The basic performance of the manufactured robot platform was verified through a water tank test, where the robot was manually operated using a joystick, and the robot motion and attitude variation that resulted from the manipulator movement were closely observed.

This paper presents a novel approach for a modular robotic platform to support power plant inspection. In a first part the paper gives an overview over the challenges and benefits of robotic inspection on power generation equipment. The industry requirements are presented and discussed on a general level. The second part of the paper focus on the modular platform itself. The overall modular architecture is described and the various robotic modules with the various design features are presented and discussed. Based on the modular architecture a local navigation and control system is introduced providing the details on the architecture, the sensors used and the different algorithms implemented. At the example of a volumetric non destructive inspection of steam turbine rotor shafts and the inspection of electro static precipitators the variety of possible system configurations is presented. The paper closes with a brief discussion of the results achieved.

This paper presents the design and development of a modular Cube Sat platform for educational and research applications in low-Earth orbit. The proposed 1U Cube Sat (10cm × 10cm × 10cm) incorporates innovative design features enhancing modularity, power efficiency, and payload adaptability while maintaining standard deployment compatibility. Our methodology encompasses comprehensive structural design, subsystem integration, and prototype development validated through finite element analysis, thermal simulations, and functional testing. Results demonstrate successful integration of command and data handling (C&DH), power distribution, and payload interface systems with a final prototype mass of 1.12 kg, leaving sufficient margin for educational payloads. The platform supports various applications including Earth observation, atmospheric measurements, and technology demonstrations, making space- based experimentation accessible to educational institutions. This research contributes to democratizing space access for academic purposes while addressing key challenges in small satellite development

ABSTRACTThis article evaluates the design of a variable impedance prosthetic (VIPr) socket for a transtibial amputee using computer-aided design and manufacturing (CAD/CAM) processes. Compliant features are seamlessly integrated into a three-dimensional printed socket to achieve lower interface peak

Minimally invasive surgery (MIS) has become the standard of care in many clinical specialties due to its ability to reduce trauma, shorten recovery times, and improve overall patient outcomes. The integration of robotic systems into MIS has further enhanced these benefits by increasing precision, dexterity, and consistency. However, conventional robotic platforms remain limited in their capacity to provide surgeons with natural tactile feedback, advanced decision support, and seamless real-time communication between human operators and robotic systems. These limitations are being addressed through the technological revolutions of Industry 4.0 and Industry 5.0. Industry 4.0 technologies such as artificial intelligence, computer vision, big data analytics, and the Internet of Things enable predictive monitoring, enhanced autonomy, and continuous performance assessment. Building on this digital foundation, Industry 5.0 emphasizes human–robot collaboration, personalization, and surgeon-centered innovation, fostering a balance between automation and human expertise. This paper explores how robotic systems for MIS are being reshaped through the convergence of these industrial paradigms. A structured analysis of advancements in haptic feedback, tactile sensing, and force monitoring demonstrates how precision can be significantly improved. The integration of computer vision and surgical data science is shown to strengthen safety through error detection and predictive analytics. Furthermore, case studies of leading robotic platforms highlight how real-time feedback loops enhance surgeon decision-making and patient outcomes. The paper also proposes a conceptual framework for aligning Industry 4.0 digitalization with Industry 5.0 human-centric design, providing a pathway for the next generation of robotic surgery. By bridging technological innovation with clinical application, this study emphasizes the transformative potential of robotic systems in MIS. It argues that the convergence of Industry 4.0 and 5.0 will not only redefine surgical precision and safety but also establish a future where real-time, surgeon-guided feedback and intelligent robotics become central to surgical practice. Ultimately, robotic surgery is positioned as a cornerstone of next-generation healthcare, combining automation with human expertise to achieve superior outcomes.

Role of Robotics in Image-Guided Trans-Arterial Interventions

Many companies are facing the challenge to extend their degree of external product differentiation whilst reducing internal complexity and costs due to increased economies of scale. Therefore, companies often develop modular product platforms. Initially, they were applied by the automotive industry but since have been exploited to various other industries, e.g. machinery and plant engineering. Based on a modular product platform a number of product variants and product generations can be efficiently derived. However, a simple transfer of the automotive approach to other cases of application is more likely to miss the full potential as well as the targets of a modular product platform. Taking into consideration that a company-specific platform concept is dependent on the influences of the environment the company interacts with, there is no universally applicable solution when it comes to modular product platforms. Hence, modular product platform concepts in industrial practice vary depending on the exogenous circumstances and individually targeted benefits, pursued by the initiative. However, at a certain level of abstraction, modular platform concepts, regardless of individual circumstances or targets, remain comparable through features describing the conceptual structuring. Therefore, the conceptual design of a modular product platform can be described by generally valid and discrete Conceptual Structural Features. From a development perspective, these features and their respective characteristics represent the conceptual design options addressing a company’s individual circumstances and targets. From a retrospective point of view, the features and their respective characteristics can equally be employed to compare existing modular product platform concepts across a vast range of industries and applications with respect to the individual target system. In this paper, seven essential Conceptual Structural Features and associated characteristics are identified. After defining the relevant terminology and reviewing adjacent fields of research, this paper focuses on a broad literature review in the field of Conceptual Structural Features of modular product platforms. A trinomial approach is used to extract the variety of different Conceptual Structural Features. In addition to the derivation of these features from different conceptual approaches for the development of a modular product platform, features of procedure induced design as well as classifying features are identified as relevant Conceptual Structural Features. Companies that configure the identified Conceptual Structural Features concerning their exogenous circumstances and individually targeted benefits are more likely to increase their level of target achievement. After the identification of Conceptual Structural Features, future research focuses on the development of a method for a context and target compliant development of a modular product platform structuring concept. Hence, companies are enabled to proactively configure the identified Conceptual Structural Features during the early development stage according to the individual platform target system as well as to the exogenous circumstances. Moreover, the results of this paper facilitate a cross-industry discussion on a comparable basis in the form of the identified Conceptual Structural Features among companies applying a modular product platform approach.

Avian influenza (AI) is a respiratory disease of birds resulting from infection with influenza A viruses. Outbreaks of the highly pathogenic form of AI (HPAI) result in the loss of millions of birds and remain a global threat to the poultry industry. The high mutation rate of the HPAI virus and a proven ability to cross the species barrier raises concerns about the pandemic potential of this zoonotic disease. The ongoing HPAI virus outbreaks in poultry, coupled with increasing reports of human cases worldwide, demonstrate the need for practical means to control its rapid spread. Poultry vaccination provides a means for prevention and control of AI by significantly decreasing the risk of viral transmission. As part of control and prevention programs against HPAI, poultry vaccines are commonly used in countries where HPAI viruses are endemic (i.e. parts of Asia and Africa). However, the currently licensed poultry vaccines against AI, which are mainly produced in embryonated chicken eggs, have potential drawbacks in terms of their production. The slow production time and requirement for at least one egg per vaccine dose make it impractical to supply sufficient doses for mass poultry vaccination during an outbreak. Vaccine manufacturing based on a microbial platform offers a promising alternative due to the speed, cost and scale associated with cultivation of the microorganism. Previously, process simulation of a bacterially-produced capsomere platform, based on the VP1 capsid protein of murine polyomavirus, has indicated that 320 million doses of capsomere vaccines could be produced within 2.3 days. Economic analysis has shown that the capsomere vaccine can be produced at a cost of less than one cent per dose, presenting a financial advantage over other vaccines in response to AI epidemics and pandemics. This thesis addresses some of the challenges associated with AI vaccine production by demonstrating the design of a novel modular capsomere platform and production process that allows a strain-matched vaccine to be delivered at a speed, cost and scale suitable for effective disease control through poultry vaccination. The major outcomes of this work were: (i) demonstration of the potential of structure-based modular capsomeres as an AI vaccine candidate; (ii) demonstration of high immunogenicity of low purity modular capsomeres prepared using a simpler method; and (iii) development of a simple and rapid non-chromatographic purification process that is economical for use in poultry vaccine production. This study is, to the best of my knowledge, the first to report on the development of a capsomere vaccine platform (designated VP1dC) that allows modularisation (a strategy for displaying target antigenic components of pathogens, known as modules, onto a platform base carrier) of a large antigenic module: a structure-based designed influenza hemagglutinin (tHA1). This modular capsomere (CaptHA1) comprises five copies of modular VP1dC-tHA1 and is efficiently produced in a stable form using Escherichia coli. CaptHA1 demonstrates high immunogenicity and confers complete protection against viral challenge when administered to chickens. The simplified production process allows CaptHA1 to be generated without solubility tags. CaptHA1 is purified using non-chromatographic methods, without the requirement of a protein refolding process, and retains its structural integrity and biological functions. The outcomes of this work contribute toward the development of a microbial-based modular capsomere platform for large antigen presentation. The platform has the potential for manufacture of vaccines for administration to poultry during AI epidemics and pandemics.

The increasing demand for product individualization and the challenges of globalization force manufacturing companies to expand their product range while keeping internal expenses low. To tackle the dichotomy between economies of scale and economies of scope, companies make use of modular product platforms and carry-over-parts. To improve the modular platform performance, it is crucial to define its structure in the early planning phase. In vertical direction, the modular platform structure defines considered technical solutions, whereas in horizontal direction, it is characterized by the products that use these solutions. When introducing or adapting modular product platforms of complex product portfolios, companies often make upfront decisions regarding the modular platform’s structure based on expert intuition. This mainly results from a lack of time, organizational restrictions and missing systematic approaches. The sheer number of product data associated with the products in the portfolio as well as the often missing transparency regarding existing components and interfaces force decision makers to decide in an intuitive approach. However, this hinders an optimal design of modular platforms and reduces the optimal performance exploitation. In order to increase modular platform performance and hence the company´s profitability, a holistic approach prior to the actual platform design process is required to determine the optimal modular platform structure for a complex product portfolio. The basis for this methodology is a generic descriptive model, which helps to describe current and planned products of a serial manufacturer’s portfolio in a structured way. The introduced methodology determines optimal modular platform scopes through systematic identification of anchor products by aid of Data Mining.