Search Results (1,045)

Wall-climbing robots have broad application potential in industrial equipment inspection, chemical storage tank maintenance, and high-altitude operations. However, their practical implementation is challenged by the robots’ adhesion requirements in complex wall environments. This study uses a systematic methodology integrating computational simulation and experimental validation to design and optimize a magnetic adsorption system for wall-climbing robots. Firstly, an adjustable suspended magnetic adhesion unit is designed to achieve intelligent control of a wall-climbing robot’s adhesion force on a wall surface. The Maxwell software (AnsysEM21.1) is used to simulate and analyze the critical parameters of the magnetic adsorption unit, including the thickness of the magnet and yoke, as well as the distance and angle between the magnet and the wall surface. Then, a magnetic wheel is designed for the wall-climbing robot based on the optimization of the structure and parameters of the magnetic adhesion unit. The absorption and demagnetization of the magnetic wheels are achieved by rotating the magnetic absorption unit. Subsequently, the simulation results are verified on the experimental platform, and adhesion performance tests are conducted on both standard flat surfaces and inclined walls. The results show that the optimized single magnetic adhesion unit gives the wall-climbing robot an adhesion force of 2767 N under normal working conditions, with a simulation experiment error margin as low as 8.3%. These results both provide theoretical guidance and highlight practical methodologies for developing high-performance magnetic adsorption systems in complex operational environments. Full article

Concrete-printing robots have become an advanced technology in the construction industry that allows the creation of complex structures, while saving materials and shortening construction time compared to traditional methods. With the structure of a concrete 3D printing robot using a concrete extruder with a screw, this mechanism provides stable flow of concrete, and less pressure fluctuation. However, using a large mass extruder changes the inertia of the joint and the mass coefficient of the arm when the mass changes, leading to a position error. With the high demands for precision and stability in the operation of 3D concrete printing robots, advanced control methods have become essential to ensure trajectory tracking and robustness in complex real-world environments. This study provides a sliding mode controller with an error and integral, and derivatives are introduced into the sliding surface to improve the stability of the robot without chattering. The controller exhibits fast convergence times and small trajectory tracking errors, at less than 0.1 mm. Simulation results show that this controller is suitable for concrete 3D printing applications, and the controller exhibits fast and good responses to continuously changing extruder mass. This enables the robot to track the expected trajectory with high accuracy. Full article

Robotics has widespread applications throughout industrial automation, autonomous vehicles, agriculture, and more. For these reasons, undergraduate education has begun to focus on preparing engineering students to directly contribute to the design and use of such systems. However, robotics is inherently multi-disciplinary and requires knowledge of controls and automation, embedded systems, sensors, signal processing, algorithms, and artificial intelligence. This makes training the future robotics workforce a challenge. In this paper, we evaluate our experiences with project-based learning approaches to teaching robotics at the undergraduate level at Miami University. Specifically, we analyze three consecutive years of capstone design projects on increasingly complex robotics design problems for multi-robot systems. We also evaluate the laboratories taught in our course “ECE 314: Elements of Robotics”. We have chosen these four experiences since they focus on the use of “cheap” first-principled robots, meaning that these robots sit on the fringe of embedded system design in that much of the student time is spent on working with a micro-controller interfacing with simple and cheap actuators and sensors. To contextualize our results, we propose the Robotic System Levels (RSL) model as a structured way to understand the levels of abstraction in robotic systems. Our main conclusion from these case studies is that, in each experience, students are exposed primarily to a subset of levels in the RSL model. Therefore, the curriculum should be designed to emphasize levels that align with educational objectives and the skills required by local industries. Full article

This study addresses the multi-robot task allocation (MRTA) problem for logistics robots operating in zone-picking warehouse environments. With the rapid growth of e-commerce and the Fourth Industrial Revolution, logistics robots are increasingly deployed to manage high-volume order fulfillment. However, efficiently assigning tasks to multiple robots is a complex and computationally intensive problem. To address this, we propose a five-step optimization framework that reduces computation time while maintaining practical applicability. The first step calculates and stores distances and paths between product locations using the A* algorithm, enabling reuse in subsequent computations. The second step performs hierarchical clustering of orders based on spatial similarity and capacity constraints to reduce the problem size. In the third step, the traveling salesman problem (TSP) is formulated to determine the optimal execution sequence within each cluster. The fourth step uses a mixed integer linear programming (MILP) model to allocate clusters to robots while minimizing the overall makespan. Finally, the fifth step incorporates battery constraints by optimizing the task sequence and partial charging schedule for each robot. Numerical experiments were conducted using up to 1000 orders and 100 robots, and the results confirmed that the proposed method is scalable and effective for large-scale scenarios. Full article

The rapid advancement of computing power, combined with the ability to collect vast amounts of data, has unlocked new possibilities for industrial applications. While traditional time–domain industrial signals generally do not allow for direct stability assessment or the detection of abnormal situations, alternative representations can reveal hidden patterns. This paper introduces time-shifted maps (TSMs) as a novel method for analyzing industrial data—an approach that is not yet widely adopted in the field. Unlike contemporary machine learning techniques, TSM relies on a simple and interpretable algorithm designed to process data from standard industrial automation systems. By creating clear, visual representations, TSM facilitates the monitoring and control of production process. Specifically, TSMs are constructed from time series data collected by an acceleration sensor mounted on a robot base. To evaluate the effectiveness of TSM, its results are compared with those obtained using classical signal processing methods, such as the fast Fourier transform (FFT) and wavelet transform. Additionally, TSMs are classified using computed correlation dimensions and entropy measures. To further validate the method, we numerically simulate three distinct anomalous scenarios and present their corresponding TSM-based graphical representations. Full article

A humanoid robot has a similar form to humans, allowing it to be deployed directly into many existing infrastructures built for people, making it highly applicable to future industries or services. However, generating motion is challenging, and it is highly affected by disturbances due to its complex dynamic characteristics. This paper proposes a method to enable a biped robot to achieve stable motion in various environments. The capture point (CP) control is used to modify the zero moment point (ZMP) to stabilize the naturally divergent dynamics of the simplified linear inverted pendulum model (LIPM) and a model predictive control (MPC) framework is implemented to generate a center of mass (COM) trajectory that tracks the adjusted ZMP. To minimize angular momentum caused by disturbances and discrepancies between the actual robot and the dynamics model, arm motion is generated through a momentum controller. Various walking simulations were conducted to verify stable whole-body motion. Full article

In this paper, a specific parametric and open-source algorithm for the direct and inverse kinematics of the UR5e Cobot is formulated by using the (n, o, a, p) transformation matrix, along with the inverse matrices, and then implemented in Matlab for numerical validation purposes. Thus, a specific robotized cell that includes novel mechatronic devices has been designed and built at LARM (Lab. of Robotics and Mechatronics) in Cassino in order to experimentally validate the proposed algorithm. In particular, many experimental points to carry out the whole automatic cycle have been detected by using the corresponding teach-pendant tool and joint positions for different UR5e Cobot poses. In addition, this consistent experimental campaign has allowed to evaluate the percentage accuracy of the robot, which can be useful for the practical applications. Therefore, the proposed kinematic model, along with the parametric and open-source algorithm, of the UR5e Cobot can be useful to simulate different applications in several robotized cells with a good reliability with respect to the real program of the robot. Full article

The Fourth Industrial Revolution has introduced “shared manufacturing” as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems. Full article

This review aims to help researchers, designers, and engineering staff extend operational times and elevate robots’ efficiency. The study represents an up-to-date summary of power electronic converters, their classification, and solutions found by leading robot manufacturers. While some advances have not yet become commonplace in mainstream robotics, their crucial role and promise are evident for expanding automation capabilities in various stationary and mobile applications. The work demonstrates two interconnected directions that are currently applied or are planned to be employed in the future as key factors contributing to reducing losses and accelerating energy transformation. The former direction relates to the implementation of wide bandgap devices that are superior to silicon-based electronics. The second trend concerns the advancements of converter topologies. In this way, the article presents how rectifiers, inverters, and their combinations provide voltage control, current management, and waveform shaping, thereby revealing their potential in improving energy utilisation in industry, transport, agriculture, households, and other sectors of vital activity. Full article

In modern engineering applications, localization and orientation play an increasingly crucial role in ensuring the successful execution of assigned tasks. Industrial robots, smart home systems, healthcare environments, nuclear facilities, agriculture, and autonomous vehicles are just a few examples of fields where localization technologies are applied. Over the years, these technologies have evolved significantly, with numerous methods being developed, proposed, and refined. This paper aims to provide a comprehensive review of the primary localization and orientation technologies available in the literature, detailing the fundamental principles on which they are based and the key algorithms used to implement them. To achieve accurate and reliable localization, fusion-based approaches are often necessary, integrating data from multiple sensors and systems or estimating hidden states. For this purpose, algorithms such as Kalman Filters, Particle Filters, or Neural Networks are usually adopted. The first part of this article presents an extensive review of localization technologies, including radio frequency, RFID, laser-based systems, vision-based techniques, light-based positioning, IMU-based methods, odometry, and ultrasound-based solutions. The second part focuses on the most widely used algorithms for localization. Finally, summary tables provide an overview of the best and most consistent accuracies reported in the literature for the investigated technologies and systems. Full article

The spacer is an important component of a transmission line and can effectively prevent wires from whipping each other and inhibit vibration. Given the complex installation conditions of multi-split lines, the installation of spacers is mainly achieved through manual work, which has the disadvantage of heavy labor intensity and a high risk factor. The robots that install two-split and four-split spacer bars cannot be applied to the complex operating conditions of six-split transmission lines. In order to improve the installation efficiency of spacers and reduce operating costs and risks, a new type of spacer-installing robot was researched based on the six-split transmission lines in this paper. Through the theoretical analysis of the wire’s arc sag, the moving device of the robot was designed. In order to improve the operating efficiency of the robot, the storage and feeding device of the six spacers was designed. A planar arm with the ability to assemble the spacer was designed. The overall design of the robot was completed by integrating the design of each unit. Through the experimental test, the results indicated that the robot was capable of installing six spacers at once, the maximum moving slope was 15 degrees, and the error rate in the spacer installation was 2.33%, which matched the manual installation of the spacers. The robot provided new ideas for the design of new transmission line engineering equipment and expanded the scope of the application of robots in the power industry. Full article

The grabbing of phosphorite rocks is an important process in the mining industry. Traditional grabbing technology based on rigid robots faces challenges such as heavy weight, low flexibility, and insufficient safety. This study presents the structural design, characteristic analysis, and modeling of a novel tailored soft robot for phosphorite grabbing (TSRPG). The TSRPG is designed with soft, flexible materials, providing flexible movement and high safety in complex environments. The design inspiration of the robot comes from humans using their thumb and index finger to hold things, and the structural design mainly focuses on the flexibility and grabbing function of the robot. The grabbing function of the TSRPG is exhibited by several actual grabbing experiments. In addition, through characteristic analysis, we explore the robot’s motion properties under various input air pressure conditions. A mathematical model of the TSRPG is developed to depict its characteristics based on the nonlinear ARX model. The developed mathematical model provides a base for promoting the practical application of the TSRPG. Full article

The globally robust control of a four-link manipulator arm (FLMA) is an important subject for a wide range of industrial applications such as COVID-19 prevention robotics, lower limb rehabilitation robotics and underwater robotics. This article uses the feedback linearized approach to stabilize the complex nonlinear FLMA without applying a nonlinear approximator that includes the fuzzy approach and neural network optimal approach. This article proposes a new approach based on the “first” derived nonlinear convergence rate formula of the FLMA to control highly nonlinear dynamics. The linear quadratic regulator (LQR) method is often applied in the balance controlling space of the underactuated manipulator. This proposed approach takes the place of the LQR approach without the necessary trial and error operations. The implications of the proposed approach are “globally” effective, whereas the Jacobian linearized approach is “locally” valid. In addition, the main innovation of the proposed approach is to perform “simultaneously” additional performances including almost disturbance decoupling performance, which takes the place of the traditional posture–energy approach and avoids some torque chattering behaviour in the swing-up space, and globally exponential stable performance, without the need to solve the Hamilton–Jacobin equation. Simulations of comparative examples show that the proposed controller is superior to the singular perturbation and fuzzy approaches. Full article

Soft robotics represents a transformative approach to automation, focusing on the development of robots constructed from flexible, compliant materials that mimic biological systems. Being different from traditional rigid robots, soft robots are engineered to adapt and operate efficiently in complex, unstructured environments, making them highly appropriate for applications that require delicate manipulation, safe human–robot interaction, and mobility on unstable terrain. The key principles, materials, and fabrication techniques of soft robotics are explored in this study, highlighting their versatility in industries such as healthcare, agriculture, and search-and-rescue operations. The essence of soft robotic systems lies in their ability to deform and respond to environmental stimuli. The system enables new paradigms in automation for tasks that demand flexibility, such as handling fragile objects, navigating narrow spaces, or interacting with humans. Emerging materials, such as elastomers, hydrogels, and shape-memory alloys, are driving innovations in actuation and sensing mechanisms, expanding the capabilities of soft robots in applications. We also examine the challenges associated with the control and energy efficiency of soft robots, as well as opportunities for integrating artificial intelligence and advanced sensing to enhance autonomous decision-making. Through case studies and experimental data, the potential of soft robotics is reviewed to revolutionize sectors requiring adaptive automation, ultimately contributing to safer, more efficient, and sustainable technological advancements than present robots. Full article

Recently, with policies aimed at strengthening domestic manufacturing and technological innovation, the robotics industry has been growing rapidly, and its applications are expanding across various industrial fields. Accordingly, the importance of high-performance speed reducers with flexibility and precision is gradually increasing. The tooth profiles used in conventional harmonic reducers have structural limitations, such as meshing discontinuity, restrictions on the radius of curvature of the tooth base, and distortion of the contact trajectory, especially when the number of teeth is small. These problems limit the design freedom of the reducer and make it difficult to secure contact stability and durability under precision driving conditions. To solve these problems, this paper proposes a new tooth profile design equation, the IH (Involute Harmonic) tooth profiles and the HTPs (Hybrid Tooth Profiles), using the cycloid curve to overcome the structural limitations of the conventional harmonic tooth profile, which is difficult to design under small-tooth-number conditions, and to enable tooth design without restrictions on the number of teeth. HTP tooth profile is a new gear tooth profile design method that utilizes IH tooth profile and cycloid curve to optimize the meshing characteristics of gears. A tooth profile design tool based on the HTP equation was developed using Python 3.13.3. The tool’s effectiveness was validated through simulations assessing tooth meshing and interference. Using the tool, an R21_z3 reducer with a single-stage high reduction ratio was designed to evaluate practical applicability. A prototype was fabricated using 3D printing with PLA material, and experimental testing confirmed the absence of meshing or interference issues, consistent with simulation results. Through this study, we verified the usefulness of the HTP tooth profile design formula and design tool using the IH tooth profile and cycloid curve, and it is expected that the proposed HTP tooth profile can be utilized as a tooth profile applicable to various reducer designs. Full article