The paper proposes a novel data-driven approximation kinematic (DAK) model to estimate the shape and opening level of a PneuNets soft gripper in relation to the applied pressure signal. The model offers suitable capabilities for implementing in real-time applications involving soft grasping planning and size recognition of fragile objects with different sizes and shapes. The proposed DAK model estimates the free bending behavior of a PneuNets actuator (soft gripper finger) based on a set of approximation functions derived from experimental data and an equivalent serial mechanism that mimics the shape of the actuator. The model was tested for a commercial PneuNets actuator with decreasing chamber height, produced by SoftGripping Co. (Hamburg, Germany). The model validation is accomplished through a set of experiments, where the shape and elementary displacements were measured using a digital image processing technique. The experimental data and the estimated data from the DAK model were compared and analyzed, respectively. The proposed approach has applicability in sensorless/self-sensing bending control algorithms of PneuNets actuators and in soft grasping applications where the robotic system must estimate the opening level of the gripper in order to be able to accomplish its task.

The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots.

The paper proposes a novel numerical method S-GUIDE that provides real-time planning of the shape of hyper-redundant robots with serial architecture by means of a guidance curve, represented in parametrized analytical form and in numerical form by a set of key points associated with the robot structure. To model the shape of the robot, the method uses an equivalent model, and a shape guidance curve obtained through a controlled adjustment of a Bézier curve. This is achieved in three computing steps were the robot equivalent structure, it’s associated kinematic parameters and the robot actuation parameters in joint space are calculated. The proposed method offers several advantages in relation with the precision, computing time and the feasibility for real-time applications. In the paper, the method accuracy, execution time, and the absolute error for different work scenarios are determined, compared and validated.

In the last 2 decades, supercapacitors have become an important component in the hybrid energy-storage system, in combination with batteries. The resulting system offers higher performance in comparison to classic energy storage systems due to the complementary advantages offered by the two components. Accurate modeling of supercapacitors and batteries is an important step in the development process of hybrid energy storage systems. The presence of proper models has a direct impact on the optimal design of the energy management strategies and in enhancing the lifespan and performance of the system. In this paper, a new approach for modeling methods for supercapacitors, named Hybrid Modeling Approach for Supercapacitors (HyMASC), is proposed. The proposed method uses an equivalent RC circuit model to capture the dynamics of the supercapacitor and introduces a hybrid method to optimally calculate the model parameters. The validation of HyMASC is done using two supercapacitors with different capacitances. The obtained numerical models were simulated in Matlab for different scenarios, and their results and performance were compared with other methods from the literature. It was observed that the proposed method offers good results with better performance, thereby contributing to the advancement in the field of supercapacitor modeling techniques.

Handling delicate agricultural products, such as tomatoes, requires careful attention from workers during harvesting, sorting, and packaging processes. This labor-intensive approach is often inefficient and susceptible to human error. A potential solution to improve efficiency is the development of automated systems capable of replacing manual labor. However, such systems face significant challenges due to the irregular shapes and fragility of these products, requiring specialized adaptable and soft gripping mechanisms. In this context, this paper introduces a parametric design methodology for 3D-printed flexible fingers in soft grippers, tailored for agricultural applications. The approach was tested in a case study that targeted soft agricultural products with diameters between 45 and 75 mm. Three finger topologies were modeled and compared to identify an optimal configuration. A prototype was then developed using 3D printing with Z-SemiFlex. Experimental tests confirmed that the prototype could grasp different fruits reliably and without surface damage. It achieved an Average Precision (AP) of 87.5% for tomatoes and 92.5% for mandarins across 80 trials. These results validate the feasibility of the proposed design methodology for fingers in soft grippers.

3D Concrete Printing (3DCP) is increasingly used for both on-site building fabrication and off-site production of structural components in controlled environments. The differences between these application contexts pose distinct requirements and constraints for the robotic systems used for material deposition. Selecting an appropriate robotic architecture, therefore, represents a critical design decision that directly influences printing quality and system performance. To address this challenge, this study proposes a multi-criteria decision-making framework for optimal selection of the robotic architecture for a 3DCP system, in accordance with its application requirements and constraints. For this, the method integrates AHP and TOPSIS, and takes into consideration factors such as technical characteristics, operational requirements, and economic costs. To demonstrate the applicability of the method, a case study was conducted to identify the most suitable robotic architecture for a laboratory-scale façade printing 3DCP system. Three robotic configurations were analyzed: a gantry system, an articulated robotic arm, and a parallel delta robot. The results showed that the articulated robotic arm achieves the highest TOPSIS closeness coefficient (CCᵢ = 0.681), outperforming the other two configurations. These findings align with existing façade-oriented 3DCP studies and indicate that articulated robotic arms are well-suited for the fabrication of geometrically complex components with higher surface quality and dimensional accuracy. The results show that the proposed framework enables transparent, application-driven decisions during early-stage robotic system design for 3DCP.