The functionality and aesthetic of 3D-printed components can be compromised if visible defects appear on their external surfaces. To overcome this issue, CNC machines were traditionally adopted for milling machining. More recently, industrial robots have been demonstrated to be a valid alternative. This study presents a robotic workstation developed for contouring machining 3D thermoplastic components printed using the material extrusion technology. The workstation adopts a collaborative robot with a novel, custom-designed, and low-cost end-effector made of a powered contouring tool integrated with three load cells for measuring the cutting forces along three perpendicular directions. The tool path planning is defined by a proposed and validated procedure. By a vision algorithm and a touch-stop operation, the 3D CAD model-based tool path is adapted to the current position and orientation of the workpiece. The experimental activity for determining the optimal set of contouring machining parameters (rotational speed, cut depth, and feed rate) and for measuring cutting forces confirms the feasibility of adopting the cobot-based solution for this application and suggests potential improvements for future works.

Robotic rehabilitation requires personalized, versatile, and efficient devices to accommodate the diverse needs of patients recovering from motor impairments. In this paper, we focus on hand rehabilitation and analyse a tendon-driven, modular, and adaptable robotic glove actuated by twisted string actuators (TSAs). The proposed solution exploits flexibility in design, allowing customization based on individual patient needs while ensuring effective assistance in hand movements.

Specifically, in this paper we investigate the kinematic relationships between tendon-driven actuators and hand motion. We provide a detailed implementation of multiple functional modules within the glove, designed to accommodate various rehabilitation exercises and adapt to different degrees of motor impairment. In addition, we present experimental tests involving a user to evaluate the system’s performance, usability, and effectiveness in facilitating hand movement. The results provide insights into the potential of TSA-driven robotic gloves for enhancing rehabilitation outcomes through a combination of precise actuation and adaptability to user’s needs.

Upper limb motor dysfunction significantly impacts daily activities and quality of life for individuals with stroke. Existing assistive robots often struggle to balance portability, ease of use, and motion assistance. This research presents WELiBot, a novel wearable end-effector-type upper limb assistive robot, designed with a 4R-5R parallel mechanism and an arc-shaped guide rail to provide controlled assistance in lifting, reaching, and circumferential motions. The study introduces the conception of the robot design, focusing on its functional requirements and mechanical structure. The kinematic and static characteristics of WELiBot were analyzed to evaluate its feasibility and effectiveness. Based on this design, a prototype with a 1/4 arc-shaped guide rail was fabricated to test motion feasibility and assistance effects. To assess its performance, electromyography experiments were conducted with four healthy participants. The results showed a significant reduction in biceps brachii muscle activity, confirming the robot’s ability to reduce user effort. Future work will focus on attaching the guide rail to the body for improved usability and refining the control strategy to enhance motion assistance and adaptability in daily life support applications.

This paper investigates a closed-loop visual servo control scheme for controlling the position of a fully constrained cable-driven parallel robot (CDPR) designed for functional rehabilitation tasks. The control system incorporates real-time position correction using an Intel RealSense camera. Our CDPR features four cables exiting from pulleys, driven by AC servomotors, to move the moving platform (MP). The focus of this work is the development of a control scheme for a closed-loop visual servoing system utilizing depth/RGB images. The developed algorithm uses this data to determine the actual Cartesian position of the MP, which is then compared to the desired position to calculate the required Cartesian displacement. This displacement is fed into the inverse kinematic model to generate the servomotor commands. Three types of trajectories (circular, square, and triangular) are used to test the controller’s compliance with its position. Compared to the open-loop control of the robot, the new control system increases positional accuracy and effectively handles cable behavior, various perturbations, and modeling errors. The obtained results showed significant improvements in control performance, notably reduced root mean square error and maximal error in terms of position.

Cartographer is an algorithm that was open sourced by Google in 2016 and adapted to multiple sensors. To address issues of the original algorithm, such as the negative impact of outlier point cloud on the scan matching, and low accuracy of position fusion. This paper preprocesses the sensor data and presents HT-Carto, an improved hybrid point-cloud filtering system, and a tightly coupled LiDAR/IMU framework based on Cartographer’s front-end. The inertial measurement unit (IMU) provides initial values for the point cloud, and the IMU pre-integration combines the scan-matched pose to construct the factors, which are added as constraints to the factor graph. The result is used to update the current pose and work as odometer residuals at the back-end. The optimization of the selected strategy during point cloud preprocessing, PassThrough, and RadiusOutlierRemoval are combined to ensure quality. An actual vehicle is used in complex indoor environment to verify the stability and robustness of HT-Carto. Compared to the Cartographer, Karto, Hector, and GMapping, HT-Carto demonstrates better localization and mapping, it can obtain a more precise trajectory.

Bilateral teleoperation has witnessed significant development since the mid-20th century, a23ressing challenges related to human presence in environments with constraints or a lack of skilled professionals. This article presents the kinematic and self-collision analyses of the quasi-spherical parallel manipulator, a three-legged parallel robot used as a haptic master device. The device is designed for remote center of motion-constrained operation in the telesurgical field. Inverse and forward kinematics are thoroughly analyzed to study working modes, singular configurations, and implement a haptic control architecture. The research explores the operative and reachable workspaces of the possible working modes, comparing them to find the most suitable one. Results highlight how the addition of the self-collision phenomenon impacts the working mode choice, drastically reducing most of the modes’ operative workspaces. An anti-collision control algorithm is finally introduced to maintain the architecture within its reachable workspace.

Automatic license plate recognition (ALPR) systems are increasingly used to solve issues related to surveillance and security. However, these systems assume constrained recognition scenarios, thereby restricting their practical use. Therefore, we address in this article the challenge of recognizing vehicle license plates (LPs) from the video feeds of a mobile security robot by proposing an efficient two-stage ALPR system. Our ALPR system combines the on-the-shelf YOLOv7x model with a novel LP recognition model, called vision transformer-based LP recognizer (ViTLPR). ViTLPR is based on the self-attention mechanism to read character sequences on LPs. To ease the deployment of our ALPR system on mobile security robots and improve its inference speed, we also propose an optimization strategy. As an additional contribution, we provide an ALPR dataset, named PGTLP-v2, collected from surveillance robots patrolling several plants. The PGTLP-v2 dataset has multiple features to cover chiefly the in-the-wild scenario. To evaluate the effectiveness of our ALPR system, experiments are carried out on the PGTLP-v2 dataset and five benchmark ALPR datasets collected from different countries. Extensive experiments demonstrate that our proposed ALPR system outperforms state-of-the-art baselines.

For precision-required robot operations, the robot’s positioning accuracy, repeatability, and stiffness characteristics should be considered. If the mechanism has the desired repeatability performance, a kinematic calibration process can enhance the positioning accuracy. However, for robot operations where high accelerations are needed, the compliance characteristics of the mechanism affect the trajectory-tracking accuracy adversely. In this paper, a novel approach is proposed to enhance the trajectory-tracking accuracy of a robot operating at high accelerations by predicting the compliant displacements when there is no physical contact of the robot with its environment. Also, this case study compares the trajectory-tracking characteristics of an over-constrained and a normal-constrained 2-degrees-of-freedom (DoF) planar parallel mechanism during high-acceleration operations up to 5 g accelerations. In addition, the influence of the end-effector’s center of mass (CoM) position along the normal of the plane is investigated in terms of its effects on the proposed trajectory-enhancing algorithm.

Trajectory optimization is a critical research area in robotics and automation, especially in manufacturing industries where mechanical systems are often required to minimize the execution time or the consumed energy. In this context, the most common mechanical systems are those with a single degree of freedom because of their simplicity and ease of control. In this paper, we present an approach for the online optimization of minimum-time and minimum-energy trajectories for a robotic system with one degree of freedom. Point-to-point motions of the considered linear axis are planned online, without idle times, by leveraging a verified dynamic model of the robotic system, which also includes an accurate identification of friction parameters. Both minimum-time and minimum-energy trajectories are considered, and the performance of the online optimization using a selected open-source optimization tool is verified in different dynamic conditions of the system. The results of extensive experiments on a belt-driven robotic axis demonstrate the feasibility and the energy-saving capabilities of the proposed approach, as well as the flexibility of the online trajectory optimization in different scenarios, while meeting the kinematics and dynamics limits of the system and guaranteeing low computational time.

This study presents an innovative framework to improve the accessibility and usability of collaborative robot programming. Building on previous research that evaluated the feasibility of using a domain-specific language based on behaviour-driven development, this paper addresses the limitations of earlier work by integrating additional features like a drag-and-drop Blockly web interface. The system enables end users to define and execute robot actions with minimal technical knowledge, making it more adaptable and intuitive. Additionally, a gesture-recognition module facilitates multimodal interaction, allowing users to control robots through natural gestures. The system was evaluated through a user study involving participants with varying levels of professional experience and little to no programming background. Results indicate significant improvements in user satisfaction, with the system usability scale overall score increasing from 7.50 to 8.67 out of a maximum of 10 and integration ratings rising from 4.42 to 4.58 out of 5. Participants completed tasks using a manageable number of blocks (5 to 8) and reported low frustration levels (mean: 8.75 out of 100) alongside moderate mental demand (mean: 38.33 out of 100). These findings demonstrate the tool’s effectiveness in reducing cognitive load, enhancing user engagement and supporting intuitive, efficient programming of collaborative robots for industrial applications.