Pasquale Ferrentino

Physical human-robot collaboration (pHRC) requires robotic skins that simultaneously ensure impact safety, as established by ISO/TS 15066, and preserve touch sensitivity for delicate human interactions. Existing soft skins enhance compliance and multimodal sensing, but their design is rarely guided by a quantitative evaluation of the trade-off between touch sensitivity and impact mitigation, and systematic impact testing against ISO/TS 15066 limits is often absent. The SkinAxis modular soft robotic skin addresses this gap by embedding a compact 3D force sensor in the soft material, directly enabling touch localization, and using a material-based design that jointly optimizes quasi-static touch sensitivity and impact safety. This purpose is achieved by combining the mechanical properties of elastomeric materials, selected through dedicated mechanical tests, to identify a stiff elastomer that efficiently transfers forces to the sensor and a highly dissipative elastomer that absorbs impact energy. To highlight the trade-off achieved by the selected materials, two main designs are benchmarked: a single-material baseline and a hybrid patch with a compliant surface, a rigid load-transmission plate, and a stiff sensor support sleeve. The designs are compared in terms of quasi-static force sensitivity ( k ε ) , touch localization performance, and maximum pressure, as well as peak-force and transmitted-energy reduction efficiencies ( P max , η F ( − ) , η E ( − ) ), measured respectively in quasi-static compression, force-indentation, and impact experiments on a human head surrogate (PILZ PRMS setup). The results demonstrate that the hybrid solution achieves higher quasi-static force sensitivity ( k ε = 0.089 ) , a more reliable model for touch localization; it reaches 44.66% reduction in peak force and 18.95% reduction of transmitted energy under impact at cobot’s operational velocity of 1 m s , with a maximum transmitted pressure of 1.11 MPa in accordance with ISO/TS 15066 limits. In parallel, finite element simulations predict the forces transmitted to the embedded force sensor in quasi-static compression and impact conditions, supporting the experimental findings.

Traditional UAVs often suffer severe structural damage during high-speed collisions. Recent research has explored sensing, control, and design strategies to enhance collision resilience. This work presents fully passive, soft continuum-lattice arms that combine visco-hyperelastic materials with nonlinear lattice geometries to trigger controlled buckling of the beams and absorb impact energy. Visco-hyperelastic parameters are identified from quasi-static and dynamic tensile tests on the material and then implemented in finite element models to optimize lattice porosity and distribution for force mitigation under quasi-static and dynamic loading conditions. Experimental validation of the simulations for the optimized beam configuration shows good agreement, with MAE 6.7% and RMSE 7% in quasi-static loading and MAE 12.1% and RMSE 14% in dynamic loading. Controlled drop tests on a quadrotor prototype demonstrate superior impact energy absorption and force mitigation compared with a bulk material design (up to a 99% increase in specific energy absorption and up to a 65% reduction in peak impact force). Furthermore, the latticed UAV is flown voluntarily and then dropped onto the ground, maintaining low thrust losses of 0.42 ± 0.26% and structural integrity after impact.

Mechanically strong, self-healing and recyclable rubber-based ionic elastomers for soft robotics hand.

Wearable robots have become increasingly prevalent in various applications, including rehabilitation, power augmentation, and assistance. However, one of the challenges in designing wearable robots is how to attach them to the human body. The attachment method should be secure, reliable, comfortable, effective, and controlled for the user. Moreover, the attachment points should not interfere with the user’s daily activities, and the attachment process should not be time-consuming or complicated. Typical straps nowadays require a time-consuming and cumbersome donning and doffing procedure from therapists for users needing rehabilitation therapy. Therefore, we propose a novel pneumatically actuated soft strap to enclose the limb and automate part of the strapping procedure. This paper proposes a preliminary design utilizing soft bending actuators for attaching physical interfaces to humans, with integrated active elements for facilitating and automating the strapping process. Finite element analysis was conducted to assess pressure requirements, bending curvature, and geometry, with simulation results demonstrating a promising agreement, with a root mean square error (RMSE) of 3.4° in bending angle. In the future, an additional locking mechanism would be required to provide the necessary holding force and fully constrain the limb.

Soft robotics modeling is a research topic that is evolving fast. Many techniques are present in literature, but most of them require analytical models with a lot of equations that are time consuming, hard to resolve, and not so easy to handle. For this reason, the help of a soft mechanics simulator is essential in this field. This article presents a tutorial on how to build a soft-robot model using an open source finite element analysis (FEA) simulator, called SOFA. This software is able to generate a simulation scene from a code written in Python or XML, so it can be used by people with different fields of competence, like mechanical knowledge, knowledge of material properties, and programming skills. As a case study, a Python simulation of a cable-driven soft actuator that makes contact with a rigid object is considered. The basic working principles of SOFA required to make a scene are explained step by step. In particular, this article shows how to simulate the mechanics and animate the bending behavior of the actuator and the importance of knowledge of the constitutive material properties for good modeling of the mechanical system. Furthermore, we will also show how to retrieve and save data from simulation, demonstrating that SOFA can easily adapt to a multidisciplinary subject, such as research in soft robotics, but can also be useful for teaching simulation and programming language principles to engineering students.

In the field of soft robotics, knowledge of material science is becoming more and more important. However, many researchers have a background in only one of both domains. To aid the understanding of the other domain, this tutorial describes the complete process from polymer synthesis over fabrication to testing of a soft finger. Enough background is provided during the tutorial such that researchers from both fields can understand and sharpen their knowledge. Self-healing polymers are used in this tutorial, showing that these polymers that were once a specialty, have become accessible for broader use. The use of self-healing polymers allows soft robots to recover from fatal damage, as shown in this tutorial, which increases their lifespan significantly.

The increasing interest in soft robotics has led to new designs that exploit the combination of multiple materials, increasing robustness and enhancing performance. However, the combination of multiple non-linear materials makes modelling and eventual control of these highly flexible systems challenging. This article presents a methodology to model multi-material soft pneumatic actuators using finite element analysis (FEA), based on (hyper)elastic constitutive laws fitted on experimental material characterisation. Modelling in SOFA, a FEA software, allows to simulate and control in real-time soft robotic structures. One of the novelties presented in this paper is the development of a new user-friendly technique for the mesh partitioning in SOFA, using MATLAB algorithms, that allow the creation of uniform and more refined meshes and a mesh domain partitioning that can be adapted for any geometry. As a case study, a cylindrical multi-material soft pneumatic actuator is considered. It is composed of an internal chamber, which is constituted of an autonomous self-healing hydrogel, modelled as a hyperelastic material, and an external elastic reinforcement, made of thermoplastic polyether-polyurethane elastomer (TPPU), approached as a linear elastic material. The simulation of the combination of a hyperelastic and a linear elastic material in a single design is another contribution of this work to the scientific literature of SOFA simulations. Finally, the multi-material model obtained with the new mesh partitioning technique is simulated in quasi-static conditions and is experimentally validated, demonstrating an accurate fit between simulation and reality.

Self-healing polymers can address the damage susceptibility in soft robotics. However, in most cases, their healing requires a heat stimulus, provided by an external device. This letter presents a self-healing soft actuator with an integrated healable flexible heater, functioning as the stimuli-providing system. The actuator is constructed out of thermoreversible elastomers that are crosslinked by the Diels-Alder (DA) reaction, which provides the healing ability. The heater is manufactured from a DA-based composite network filled with 20 wt% carbon black to provide electrically conductive properties for resistive Joule heating. The flexibility of the heater does not compromise the actuator performance upon integration and the self-healing properties of both heater and actuator allow for damage repair. This includes very large damages, as both heater and actuator can recover (near 100%) from being cut completely in two pieces, using Joule heating at 90°C with a bias voltage of about 30 V. The embedded heater avoids the need for external intervention in the healing process, and provides healing quality assessment and a healing on-demand mechanism, paving the way for an optimum healing solution of damage resilient soft robots that require heat as a healing stimulus.

Recent advances in soft continuum robots revealed the need for accurate models, required to develop advanced control strategies. In this article, a general methodology is presented that allows to create an accurate inverse kinematic control based on hyperelastic models, fitted on mechanical material properties. This methodology is based on finite element analysis (FEA) and links the mechanical properties of the material to the simulated behavior of a Pneunet actuator. This procedure is valid for any sort of hyperelastic material used in soft robotics, however, as a case study, a specific material with a self-healing ability is considered. This elastomeric polymer can recover from macroscopic damages without losing its mechanical performances, after undergoing a heat treatment. In this article, an accurate characterization of the mechanical behavior of this material is provided, involving mechanical testing, both in tensile and compression. On this experimental characterization data constitutive laws are fitted, using a FEA simulator. The robustness of this material modeling is shown, refitting the curve for material samples that were exposed to multiple damage-healing cycles. With this obtained constitutive model, a FEA simulation of a bending soft pneumatic actuator is developed. The simulation results are experimentally validated with a dedicated test bench consisting of a pressure control unit and a motion tracking camera. Using this validated model, an inverse kinematic control is developed, including the FEA simulation in the control scheme.

Although substantial progresses have been made in robot-assisted laparoscopic surgery, the graspers for existing surgical systems generally remain non-sensorized forceps design with limited functions. This paper presents the design, development and preliminary evaluation of the MUSHA Hand II, a multifunctional hand with force sensors for robot-assisted laparoscopic surgery. The proposed hand has three snake-like underactuated fingers that can be folded into a 12 mm cylindrical form. Each finger has a three-axis force sensor, to provide force information. After been deployed into an abdominal cavity, the hand can be configured to either grasper mode, retractor mode or palpation mode for different tasks. Underactuated finger design enhances the adaptivity in grasping and the compliance in interaction with the environment. In addition, fingertip force sensors can be utilized for palpation to obtain a real-time stiffness map of organs. Using the da Vinci Research Kit (dVRK) as a robotic testbed, the functionality of the hand has been demonstrated and experiments have been conducted, including robotic palpation and organ manipulation. The results suggest that the hand can effectively enhance the functionality of a robotic surgical system and overcome the limits on force sensing introduced by the use of robots in laparoscopic surgery.