Among the many challenges of parallel kinematic manipulators, achieving high-speed and accurate control remains crucial. Estimating their dynamic properties is essential for designing precise and efficient control schemes. Conventional methods for dynamic model identification have been effective, though deep learning approaches have historically faced limitations due to data inefficiencies. However, recent advancements in physics-informed neural networks (PINNs) offer a way to improve both control and the extraction of interpretable physical properties from these robots. In this work, we propose and validate a PINN-based dynamic model for a Delta parallel robot, specifically the ABB IRB 360-6/1600. Our approach incorporates known physical properties, such as mass matrix sparsity, to improve accuracy and computational efficiency in dynamic model identification. To the best of our knowledge, this is the first study applying PINNs to model parallel robots. The method is validated experimentally, and its performance is compared to a validated identification technique for physically consistent identification, demonstrating the effectiveness of this approach for real-world applications in parallel robots.

The determination of the dynamic properties of a robot is especially important for designing highly accurate and efficient control systems. Conventional methods for dynamic model identification have proven to be effective, where deep learning (DL) approaches have shown limits due to data inefficiencies. However, thanks to novel physics-informed DL architectures, such as Deep Lagrangian Networks (DeLaN) [1], it is possible to control and extract interpretable physical information of a robot. This paper introduces an augmented DeLaN architecture for linear viscous and non-symmetrical Coulomb friction identification, which also learns motor parameters such as rotor inertia. An approach is proposed for comparing this method with the conventional dynamic identification and previous DeLaN implementations. Moreover, our friction and rotor inertia identification is validated, and the performance of our model is analyzed with a real robot (UR5e).

High precision industrial applications call for equally precise functioning of industrial manipulators, which in turn requires accurate modeling of the manipulators. This paper carries out a detailed study on the modeling of industrial manipulators with elastic joints to improve their accuracy. In particular, the effect of adopting a simple harmonic drive (HD) model and ignoring a dynamic effect called low inertia coupling between the actuators and links on the model accuracy has been analyzed from a parameter estimation perspective. Since the aforementioned model characteristics have been generally ignored for high gear reduction ratios, this study is carried out with five different reduction ratios ranging from low to high, where three different models of a three-joints elastic manipulator are considered. The accuracy of the models is compared using the torque performance metrics of a predefined joint motion of the robot. Furthermore, the impact of the models with different accuracy is assessed by carrying out a state-of-the-art dynamic parameter estimation, and the resulting errors are compared to ascertain the merits of adopting a detailed elastic dynamic model of a manipulator.

One major problem in industrial automation is the workload balancing problem. It consists of making the robots or, more generally, the machines, involved in the assembly process to work exactly the same, either by picking and placing the same number of pieces or by having the same number of operational cycles. This paper presents a novel strategy for solving such a problem by means of an evolutionary algorithm. The specific application of this strategy is to balance the workload of a pick-and-place process developed in the facilities of the industrial company Fameccanica Spa Data within the framework of an industrial project between the company and our research group. The novelties concerning the state-of-the-art contributions are: (1) instead of using an explicit fitness function, the candidate solutions at each iteration are evaluated by using a simulation of the entire process; (2) the parameters optimized are the velocity and acceleration of the robots involved in the line and (3) the strategy includes an algorithm for distributing the workload between the robots during the process.

Cable-driven joints proved to be an effective solution in a wide variety of applications ranging from medical to industrial fields where light structures, interaction with unstructured and constrained environments and precise motion are required. These requirements are achieved by moving the actuators from joints to the robot chassis. Despite these positive properties a cable-driven robotic arm requires a complex cable routing within the entire structure to transmit motion to all joints. The main effect of this routing is a friction phenomenon which reduces the accuracy of the motion of the robotic device. In this paper a bond-graph approach is presented to model a family of cable-driven joints including a novel friction model that can be easily implemented into a control algorithm to compensate the friction forces induced by the rope sliding into bushings.

Robotic arms can perform grasping actions thanks to their “dexteorus” part, i.e. the gripper. Among the various categories, nowadays pneumatic grippers became the most employed in industry, as they have low cost and little bulkiness. Despite their simplicity, controlling the force applied by these grippers is not straightforward due to the dependence of such a force on the air pressure in the gripper chambers. As a result, it is still tricky to implement closed-loop force control for pneumatic grippers. This paper intends to deliver a control scheme relying on the force measurement to control pneumatic grippers. The force might be measured through a commercial sensor (e.g. a load cell) and fed back to close the control loop. This includes a calibration which maps the force-pressure relation taking into account both desired force and length of the gripper fingers. The control scheme exploits two different pressure regulators to precisely adjust the air pressure inside the gripper chambers (i.e. opening and closing chambers). To this aim, a quadratic programming algorithm is employed. The control scheme performance revealed to be good: results will be shown in terms of gripper response to sinusoidal and step inputs, along with the pressure-force characterization.

This paper proposes a novel gripper or end-effector named VARO-fi (VARiable Orientable fingers with translation), with the aim of obtaining human like prehensile manoeuvre such as, in-hand manipulation. The 4 fingered VARO-fi consists of 9 degrees of freedom and it can perform several in-hand manipulation tasks which have been described in this paper. Moreover, the gripper is a simplification of previously proposed gripper platform called Dexclar. The derivation of VARO-fi has been presented and its capabilities have been demonstrated by experiments. Although a generic convex payload is considered as a primitive in the design of VARO-fi however, it is capable to address manipulation for other regular shaped payloads, which has been proven by experiments. A comparison is also illustrated in order to underline the strength of the novel gripper with respect to the state of the art.

We present the first autonomous endoscope for the visual inspection of very small ducts and cavities, up to a 6-mm diameter. The system has been designed, implemented, and tested in a challenging industrial scenario and in strict collaboration with an avionic industry partner. The inspected objects are metallic gearboxes eventually presenting different residuals (e.g., sand, machining swarfs, and metallic dust) inside the oil ducts. The automatic system is actuated by a robotic arm that moves the endoscope with a microcamera inside the gearbox duct, while a deep-learning-based spatio-temporal image analysis module detects, classifies, and localizes defects in real time. Feedback is given to the robotic arm in order to move or extract the endoscope given the detected anomalies. Evaluation provides a detection rate of nearly 98% given different tests with different types of residuals and duct structures.

Robotics in the last decades is moving towards bioinspired solutions in order to develop systems increasingly integrated with the human environment. Among them, legged robots fascinates more and more researchers thanks to their ability of moving in unstructured environment such as the ones typical of earthquakes, where in the near future robots are planned to be send to help humans while performing dangerous tasks. On the base of this findings, the authors propose a novel concept for a jumping humanoid leg, based on the key role played from the structural flexibility. The geometric and dynamic features of this leg have been selected thanks to a targeted set of numerical simulations. An extensive campaign of experimental tests useful for the validation of the numerical model here presented will be a matter of future works.

Developing grasping devices with the capabilities to carry out dexterous tasks similar to human hand are being studied for many decades. To this aim, mathematical analysis such as control of multi-fingered gripper, grasp synthesis algorithms, contact types and their interactions have been explicitly addressed by many researchers. Since human hands are dexterous due to the complex integration of control and numerous sensors, hence they are naturally adaptable to grasp, in-hand manipulation of plurality of object by their construction. On the other hand, artificial grippers require priori knowledge of the payload geometry and configuration to maneuver grasping and manipulation tasks at the very first place. Moreover, theoretical analysis, such as contact kinematics, grasp stability cannot predict the nonholonomic behaviors, and therefore, uncertainties are always present to restrict a maneuver, even though the gripper is kinematically feasible of doing the task. Hence, in general, industrial grippers do exploit simpler mechanisms with least number of fingers and tend to avoid soft materials in the construction primarily to achieve dexterity, reliability, repeatability and speed in the process. However, in-hand manipulation of objects urges certain degrees of flexibility in the gripper design; which is difficult to obtain from a rigid structure and also the use of non-rigid materials reduce speed, accuracy and performance. In this research, a gripper platform named Dexclar (DEXterous reConfigurable moduLAR) is proposed, which addresses the dilemma by combining mechanism and modularity, evaluating payload centric requirements.

In packaging industry, the duration of the carton folding plays a fundamental role in the production process; in particular in the erection process when each panel rotates around the die-pressed lines called creases. Their bending response can be very complex, depending on forming and environment conditions. The crease mechanical properties, such as geometrical parameters, temperature, moisture and folding speed, influence the overall production. It is therefore necessary to control all of these parameters, from both a theoretical and experimental point of view. About this, an experimental setup is expressly designed and built w.r.t. the rotation angle for a paper around the respective crease. The results of this research allow demonstrating the reliability of the experimental setup, the substantial negligibility of the geometrical errors and determining the number of sample so that the dispersion is compatible with the experimental errors. Then the test-ring is suitable for carton folding investigation.

The computation of skin forces and deformations for tactile rendering requires an accurate model of the extremely nonlinear behavior of the skin. In this work, we investigate the characterization of finger mechanics with the goal of designing accurate nonlinear models for tactile rendering. First, we describe a measurement setup that enables the acquisition of contact force and contact area in the context of controlled finger indentation experiments. Second, we describe an optimization procedure that estimates the parameters of strain-limiting deformation models that match best the acquired data. We show that the acquisition setup allows the measurement of force and area information with high repeatability, and the estimation method reaches nonlinear models that match the measured data with high accuracy.

An increasing interest towards functionally independent and self-reconfigurable robots featured the research direction in the recent past. Such attention is well explained by the characteristics of flexibility and cost-effectiveness which distinguish these modular machines. This scenario framed the birth of many different devices characterized by simplicity of use and versatility. The present manuscript follows these trends by presenting a newly conceived device suitable for being used as a limb, as well as a planar two degrees of freedom manipulator. The features of both parallel and serial kinematic mechanisms have been exploited to the aim of obtaining a device as much as possible independent from the frame to whom it is connected. Formal aspects about position and velocity kinematics are addressed and a specifically built prototype is then used for experimental tests on the new limb.

In electronic manufacturing system, the design of the robotic hand with sufficient dexterity and configuration is important for the successful accomplishment of the assembly task. Due to the growing demand from high-mix manufacturing industry, it is difficult for the traditional robot to grasp a large number of assembly parts or tools having cylinder shapes with correct postures. In this research, a novel jaw like gripper with human-sized anthropomorphic features is designed for in-hand precise positioning and twisting online. It retains the simplicity feature of traditional industrial grippers and dexterity features of dexterous grippers. It can apply a constant gripping force on assembly parts and performs reliable twisting movement within limited time to meet the industrial requirements. Manipulating several cylindrical assembly parts by robot, as an experimental case in this paper, is studied to evaluate its performance. The effectiveness of proposed gripper design and mechanical analysis is proved by the simulation and experimental results.

In electronic manufacturing system, the design of the robotic hand with sufficient dexterity and configuration is important for the successful accomplishment of the assembly task. It is significant that the robot can grasp assembly parts and do some simple in-hand manipulation so as to fit them with the package slots. In this research, we study the process of precise in-hand posture transition problem using a novel jaw like gripper with human-sized anthropomorphic features. We transform the in-hand manipulation problem into a series of static grasping problems. Then we study the successful twisting condition on each grasp frame by analyzing its dynamic performance and requirements. Based on this data-driven idea, simulation and experimental data is obtained from both successful and failed trials. Finally, we create the distribution of parameters grasp map for successful twisting.

In human and robot collaborative hybrid assembly cell as we proposed, it is important to develop automatic subtask allocation strategy for human and robot in usage of their advantages. We introduce a folk-joint task model that describes the sequential and parallel features and logic restriction of human and robot collaboration appropriately. To preserve a cost-effectiveness level of task allocation, we develop a logic mathematic method to quantitatively describe this discrete-event system by considering the system tradeoff between the assembly time cost and payment cost. A genetic based revolutionary algorithm is developed for real-time and reliable subtask allocation to meet the required cost-effectiveness. This task allocation strategy is built for a human worker and collaborates with various robot co-workers to meet the small production situation in future. The performance of proposed algorithm is experimentally studied, and the cost-effectiveness is analyzed comparatively on an electronic assembly case.

Recent investigations show that compliant systems can be more safe and energy-efficient than conventional stiff actuated systems. As a result, researchers are increasingly implementing compliance within actuation systems using a variety of mechanisms. In general, these actuators can be grouped in 2 main categories. The first category includes all the actuation systems with a compliant element connected in series (SEA), while the second group contains all those systems that employ two actuators placed antagonistically. In both designs the ability to regulate the stiffness is essential in order to meet safety and/or performance demands. Energy consumption is a very important aspect to be considered, especially in autonomous robots. This paper presents a theoretical study on the energy consumption of variable stiffness actuators, comparing the amount of energy required in order to perform a certain task.