Search Results (8)

This research introduces a virtual reality (VR) training system for improving human–robot collaboration (HRC) in industrial disassembly tasks, particularly for e-waste recycling. Conventional training approaches frequently fail to provide sufficient adaptability, immediate feedback, or scalable solutions for complex industrial workflows. The implementation leverages Quest Pro’s body-tracking capabilities to enable ergonomic, immersive interactions with planned eye-tracking integration for improved interactivity and accuracy. The Niryo One robot aids users in hands-on disassembly while generating synthetic data to refine robot motion planning models. A Robot Operating System (ROS) bridge enables the seamless simulation and control of various robotic platforms using Unified Robotics Description Format (URDF) files, bridging virtual and physical training environments. A Long Short-Term Memory (LSTM) model predicts user interactions and robotic motions, optimizing trajectory planning and minimizing errors. Monte Carlo dropout-based uncertainty estimation enhances prediction reliability, ensuring adaptability to dynamic user behavior. Initial technical validation demonstrates the platform’s potential, with preliminary testing showing promising results in task execution efficiency and human–robot motion alignment, though comprehensive user studies remain for future work. Limitations include the lack of multi-user scenarios, potential tracking inaccuracies, and the need for further real-world validation. This system establishes a sandbox training framework for HRC in disassembly, leveraging VR and AI-driven feedback to improve skill acquisition, task efficiency, and training scalability across industrial applications. Full article

With the decreasing and aging agricultural workforce, fruit harvesting robots equipped with higher degrees of freedom (DoF) manipulators are seen as a promising solution for performing harvesting operations in unstructured and complex orchard environments. In such a complex environment, guiding the end-effector from its starting position to the target fruit while avoiding obstacles poses a significant challenge for path planning in automatic harvesting. However, existing studies often rely on manually constructed environmental map models and face limitations in planning efficiency and computational cost. Therefore, in this study, we introduced a collision-free path planning method for a 6-DoF orchard harvesting manipulator using an RGB-D camera and the Bi-RRT algorithm. First, by transforming the RGB-D camera’s point cloud data into collision geometries, we achieved 3D obstacle map reconstruction, allowing the harvesting robot to detect obstacles within its workspace. Second, by adopting the URDF format, we built the manipulator’s simulation model to be inserted with the reconstructed 3D obstacle map environment. Third, the Bi-RRT algorithm was introduced for path planning, which performs bidirectional expansion simultaneously from the start and targets configurations based on the principles of the RRT algorithm, thereby effectively shortening the time required to reach the target. Subsequently, a validation and comparison experiment were conducted in an artificial orchard. The experimental results validated our method, with the Bi-RRT algorithm achieving reliable collision-free path planning across all experimental sets. On average, it required just 0.806 s and generated 12.9 nodes per path, showing greater efficiency in path generation compared to the Sparse Bayesian Learning (SBL) algorithm, which required 0.870 s and generated 15.1 nodes per path. This method proved to be both effective and fast, providing meaningful guidance for implementing path planning for a 6-DoF manipulator in orchard harvesting tasks. Full article

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the limitations of traditional industrial operations. This paper explores the application and evolution of metaverse technology in the field of industrial robotics, focusing on the realization of virtual–real integration and human–machine collaboration. It proposes a design framework for a virtual–real interaction system based on the ROS and WEB technologies, supporting robot connectivity, posture display, coordinate axis conversion, and cross-platform multi-robot loading. This paper emphasizes the study of two key technologies for the system: virtual–real model communication and virtual–real model transformation. A general communication mechanism is designed and implemented based on the ROS, using the ROS topic subscription to achieve connection and real-time data communication between physical robots and virtual models, and utilizing URDF model transformation technology for model invocation and display. Compared with traditional simulation software, i.e., KUKA Sim PRO (version 1.1) and RobotStudio (version 6.08), the system improves model loading by 45.58% and 24.72%, and the drive response by 41.50% and 28.75%. This system not only supports virtual simulation and training but also enables the operation of physical industrial robots, provides persistent data storage, and supports action reproduction and offline data analysis and decision making. Full article

Robots equipped with legs have significant potential for real-world applications. Many industries, including those concerned with instruction, aid, security, and surveillance, have shown interest in legged robots. However, these robots are typically incredibly complicated and expensive to purchase. Iron Dog Mini is a low-cost, easily replicated, and modular quadruped robot built for training, security, and surveillance. To keep the price low and its upkeep simple, we designed our quadruped robot in a modular manner. We provide a comparative study of robotic manufacturing cost between our proposed robot and previously established robots. We were able to create a compact femur and tibia structure with sufficient load-bearing capacity. To improve stability and motion efficiency, we considered the novel Watt six-bar linkage mechanism. Using the SolidWorks modeling software, we analyzed the structural integrity of the robot’s components, considering their respective material properties. Furthermore, our research involved developing URDF data for our quadruped robot based on its CAD model. Its gait trajectory is planned using a 14-point Bezier curve. We demonstrate the operation of the simulation model and briefly discuss the robot’s kinematics. Computational methods are emphasized in this research, coupled with the simulation of kinematic and dynamic performances and analytical/numerical modeling. Full article

This paper proposes the modeling and simulation of the unmanned driving system for underground load haul dump vehicles based on Gazebo/Ros. Firstly, the kinematics model of the load haul dump vehicle is derived. Then, the model of each part of the load haul dump vehicle is established based on SolidWorks and the model of the load haul dump vehicle is established by connecting the parts through a unified robot description format (URDF) file. Finally, the laneway model is established by using alpha shape to realize the modeling of the operating environment of the load haul dump vehicle. The speed, angular speed, bucket lifting, and bucket flipping of the load haul dump vehicle are controlled using PID. The experimental results show that: The control errors of the speed and angular speed of the load haul dump vehicle are 0.283 m/s and 0.010 rad/s, respectively. The control error of the lifting bucket is 0.025 m and that of the flipping bucket is 0.015 m. The angular velocity control error of the simulation system relative to the actual system is 0.330 and 0.106 m/s, respectively. The error between the SLAM of the simulation system and the actual system and the measured value is 0.917 and 3.44 m, respectively. The control performance of the load haul dump vehicle in the simulation system is good. Therefore, automatic driving algorithms can be studied and tested in this simulation platform. Full article

Aiming at the problems of low mapping accuracy, slow path planning efficiency, and high radar frequency requirements in the process of mobile robot mapping and navigation in an indoor environment, this paper proposes a four-wheel drive adaptive robot positioning and navigation system based on ROS. By comparing and analyzing the mapping effects of various 2D-SLAM algorithms (Gmapping, Karto SLAM, and Hector SLAM), the Karto SLAM algorithm is used for map building. By comparing the Dijkstra algorithm with the A* algorithm, the A* algorithm is used for heuristic searches, which improves the efficiency of path planning. The DWA algorithm is used for local path planning, and real-time path planning is carried out by combining sensor data, which have a good obstacle avoidance performance. The mathematical model of four-wheel adaptive robot sliding steering was established, and the URDF model of the mobile robot was established under a ROS system. The map environment was built in Gazebo, and the simulation experiment was carried out by integrating lidar and odometer data, so as to realize the functions of mobile robot scanning mapping and autonomous obstacle avoidance navigation. The communication between the ROS system and STM32 is realized, the packaging of the ROS chassis node is completed, and the ROS chassis node has the function of receiving speed commands and feeding back odometer data and TF transformation, and the slip rate of the four-wheel robot in situ steering is successfully measured, making the chassis pose more accurate. Simulation tests and experimental verification show that the system has a high precision in environment map building and can achieve accurate navigation tasks. Full article

Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots’ object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots. Full article

The Robot Operating System (ROS) provides roboticists with a standardized and distributed framework for real-time communication between robotic systems using a microkernel environment. This paper looks at how ROS metadata, Unified Robot Description Format (URDF), Semantic Robot Description Format (SRDF), and its message description language, can be used to identify key robot characteristics to inform User Interface (UI) design for the teleoperation of heterogeneous robot teams. Logical relationships between UI components and robot characteristics are defined by a set of relationship rules created using relevant and available information including developer expertise and ROS metadata. This provides a significant opportunity to move towards a rule-driven approach for generating the designs of teleoperation UIs; in particular the reduction of the number of different UI configurations required to teleoperate each individual robot within a heterogeneous robot team. This approach is based on using an underlying rule set identifying robots that can be teleoperated using the same UI configuration due to having the same or similar robot characteristics. Aside from reducing the number of different UI configurations an operator needs to be familiar with, this approach also supports consistency in UI configurations when a teleoperator is periodically switching between different robots. To achieve this aim, a Matlab toolbox is developed providing users with the ability to define rules specifying the relationship between robot characteristics and UI components. Once rules are defined, selections that best describe the characteristics of the robot type within a particular heterogeneous robot team can be made. A main advantage of this approach is that rather than specifying discrete robots comprising the team, the user can specify characteristics of the team more generally allowing the system to deal with slight variations that may occur in the future. In fact, by using the defined relationship rules and characteristic selections, the toolbox can automatically identify a reduced set of UI configurations required to control possible robot team configurations, as opposed to the traditional ad-hoc approach to teleoperation UI design. In the results section, three test cases are presented to demonstrate how the selection of different robot characteristics builds a number of robot characteristic combinations, and how the relationship rules are used to determine a reduced set of required UI configurations needed to control each individual robot in the robot team. Full article