It is often easier to manufacture and experimentally characterize a soft robotic actuator than it is to construct a theoretical or finite element model to understand its mechanics. In many cases, soft actuators with reinforcement materials need to be homogenized to ascertain models for modeling and control, or a high fidelity model is needed, which is often computationally intractable to solve in real time. The modeling of soft robotic actuators should consider the micro-and mesoscale mechanics that impact the macroscale actuator response. Building on recent developments in both data-driven control and full-field measurement techniques of local strain deformations, we propose using Digital Image Correlation (DIC) to ascertain observables to relate the length scales in soft robotic actuators for rapid dynamic modeling. Using flexible structures with rotating squares as a demonstrating example, we fabricate tubular actuators and characterize their motions using DIC. Nonlinear models relating the length scales are constructed from observables ascertained via DIC using Sparse Identification of Nonlinear Dynamical Systems (SINDy). We demonstrate the goodness-of-fit of several models and discuss limitations in extending these models for control. This work provides a general recipe for the data-driven identification of dynamic models for soft robotic actuators from observables at multiple length scales.
Recent catheter designs have considered the use of magnetically active soft continuum robots controlled via external magnetic actuation systems. These have proven effective for minimally invasive applications due to their soft structure, high dexterity, and wireless actuation, all at small scales. Single and multiple magnetic segment configurations have been proposed to allow tip-only control or full shapeforming, respectively. However, these approaches utilize fixed-length magnetic segments, which require advancement from the proximal end to enhance the reachability and workspace coverage of the steerable tip. To address this limitation, we propose a dual-actuated catheter tip design comprising a 3D-printed soft tendon-driven helical body for length adaptability coupled with a tip-mounted permanent magnet for steering. We achieve a compact design of 2.4 mm outer diameter and 18 mm nominal length, suitable for carotid artery aneurysm intervention. An existing analytical model based on the Euler-Bernoulli beam theory is adapted to predict tip deflection and tip angle under magnetic actuation at three different actuation lengths: contracted, normal, and extended. We demonstrate tip length actuation spanning from a 37.2% reduction to a 35.6% extension, corresponding to 11.3 mm and 24.4 mm when fully contracted and extended, respectively. The experimental magnetic actuation shows a maximum deflection angle of 89.5 degrees with >68 degrees achievable across all length configurations. We subsequently demonstrate how the proposed hybrid tendon and magnetic actuation approach enables enhanced workspace access when compared to a fixed-length design, improving access within simple and more complex aneurysm phantoms.
This paper presents the design, development, and control of a low-cost two-flexible-finger robotic gripper for grasping fragile objects. The gripper is fabricated using 3D-printed materials, with flexible fingers made from TPU (95A shore hardness), equipped with conductive gel-based contact sensors and strain gauges for force feedback. The system is driven by a DC motor, and the control scheme includes position-force control to ensure delicate grasping. The gripper operates in two stages: object approach and controlled force application, with adaptive control parameters based on sensor feedback to prevent damage to fragile items. Experimental tests demonstrate the gripper’s capability to grasp fragile objects such as Christmas ornaments and eggs, validating the design and control strategy.
Soft-growing robots excel in navigating complex environments due to their flexibility and significant length extension, making them ideal for data collection where human access is restricted by hazards. However, integrating the reliable power and signal connections needed for these sensors without compromising the robot’s growth and steering abilities has proven challenging in previous designs. In this paper, the impact of wire attachment and routing paths on the performance of soft-growing robots is characterized by comparing straight and helical configurations in both a quasi-static bending test for steering and an eversion test for growth. Results show that helical routing improves steering by maintaining flexibility and maneuverability, whereas straight wires reduce steering and cause unpredictable rolling. While all configurations could grow, the effects of wire stiffness and configuration on growth were more nuanced; in some situations, the wire’s structure aided growth, while in others the wire hindered it. Overall, a medium helical pitch effectively balanced growth speed and steering capability, retaining functionality while adding the possibility of electrical integration. These findings support the integration of wires in soft-growing robots to ensure robust electrical connection with minimal performance impact.
Fluidic elastomer actuators have emerged as one of the most promising candidates for modular and reconfigurable soft robotics devices, especially for wearable and healthcare applications, due to their inherent compliance, robustness, cost-effectiveness, and ease of fabrication. However, a significant challenge in the widespread adoption of fluid-driven soft robots is their reliance on bulky and heavy external pumps and compressors, which fundamentally limit their portability and versatility. To address this challenge, our research introduces an approach utilizing the liquid-vapor phase transition of a low-boiling point liquid to develop a portable pump, without using any rigid mechanical moving component. This low voltage-driven electrothermal pump employs the Peltier effect for rapid and reversible active heating and cooling, allowing for more efficient heat transfer compared to joule heating and natural passive cooling. A maximum output pressure of 43.2 kPa and a maximum flow of 72 ml/min is achieved using a battery. This pump stands out for its portability, versatility, and potential to function as a modular self-contained fluidic source for soft robots. It is poised to revolutionize various applications, including home-based wearable devices, thermally active clothing, and autonomous untethered soft robots.
Simulation-driven optimisation is increasingly utilised in the design and control development of soft robots and actuators. However, setting up such an optimisation pipeline is complex, often requiring the integration of multiple software tools and algorithms, which can compromise robustness.To address these challenges, we propose a single integrated environment that allows for the flexible description of soft robots and actuators. Our system robustly handles geometry generation, meshing, simulation, and optimisation.We achieve this by using implicit geometry shape functions and voxelisation to create tetrahedral meshes, followed by Extended Position-Based Dynamics (XPBD) to simulate soft materials. As XPBD lacks physical constants, we use an evolutionary optimisation algorithm to calibrate simulation parameters to real-world behaviour and assess how geometry and voxel count affect simulation accuracy. Once calibrated, we find these parameters enable accurate simulations of more complex geometries.Finally, we validate the effectiveness of our integrated environment by optimising a cylindrical soft actuator, demonstrating its potential as an optimisation platform for the field of soft robotics.
Rope is a versatile tool that has been used throughout history for numerous applications, thanks to its ability to conform to different shapes and manipulate objects of various length scales, textures, and masses. Rope could therefore be a viable tool for expanding the functionality of robots operating in human environments. In this paper, we present a rope-based manipulator that can be equipped to robots’ end-effectors and used to ensnare objects and interact with environmental infrastructure. The manipulator operates by accelerating a loop of rope into a free-floating lasso-like shape whose size can be adjusted on demand. We study key variables that govern the loop shape, assess the rope manipulator’s ability to grasp a variety objects, propose a physical model for simulation of rope manipulation, and demonstrate the concept’s application potential when attached to a quadrupedal robot. We find that the manipulator enables wrangling of diverse objects and can be used to functionalize the existing appendages of a teleoperated quadruped robot, empowering the robot to pick up tools, open doors, and engage in manipulation of large objects. Overall, this work provides a foundation for creating and controlling a new class of rope-based robotic manipulators, serving as a testament to how passive mechanical features may be exploited to enrich manipulation capabilities.
Continuum robot researchers must often create entirely new prototypes for their applications as existing continuum robot designs do not meet their requirements, especially for industrial applications. To address this issue, we leverage a parametric design for modular units, which can be utilized to create a Tendon-Driven Continuum Robot (TDCR) tailored to specific task requirements. Using a Design of Experiment approach, we identify the relationship between the modular units’ stiffness and dimensions, establishing a mathematical framework to guide TDCR parametrization. A structured workflow is provided to guide the development of TDCRs, from the definition of the task objectives to the experimental validation. For demonstration, an aircraft wing inspection task was selected and an 816 mm TDCR was prototyped using the parametric design approach. The TDCR successfully inspected all target areas in a 1:3 scaled Cessna 172 wing model, yielding a 100% coverage rate.
This work proposes the first derivation, implementation, and experimental validation of magnetic-based proprioceptive sensing method for soft robotic applications. In our proposed approach, the magnetic sensing system measures gradient tensor contractions that can be directly related to the shape of a deformable plastering tool. Custom-designed and 3D-printed plastering tool embeds two identical permanent magnets that generate a non-symmetric magnetic field tracked by the proposed sensor. Seamless real-time control is enabled with the sensor sampling rate of 1 kHz. Classical linear control is synthesized for the scraper bending angle and orientation control. The tool, sensor, and the proposed control system are validated on robotic plastering and painting tasks.
Vine robots enable safe navigation through sensitive environments due to their inherent compliance and their mechanism for growth via tip eversion. However, the compliance of these soft robots limits their ability to withstand significant loads, preventing them from performing tasks that require lifting or manipulating objects. To expand the range of applications for vine robots, they must therefore be able to increase their stiffness to prevent collapse. In this paper, we present a low profile, active stiffening mechanism integrated into the skin of the vine robot. The mechanism consists of a set of axially stacked pneumatic pouches that generate an axial force in the robot's body to mitigate wrinkling in the robot's material, enabling the robot to withstand larger loads without collapsing. We characterized the burst pressure, stiffness, and transverse collapse load of the vine robot with our stiffening mechanism and demonstrated the ability of the robot to simultaneously stiffen and grow. Our active stiffening mechanism is implemented in an 18 mm diameter vine robot and achieves a 680% increase in stiffness.
High aspect ratios are a common feature in biological systems like muscle fibers, tentacles, or annelids that inspire novel applications in artificial muscles, grasping, manipulation, and locomotion. This paper explores interoceptive and exteroceptive sensing methods for high-aspect-ratio soft robots to overcome the limitation of externalized sensing and control, which is currently typical for such robots. We present a design and manufacturing process for sensorized soft robots (aspect ratio similar to 17) with an integrated stretchable carbon microparticle proprioception sensor and phototransistor-based exteroceptive layer for low-resolution ambient light detection. We show that our interoceptive sensor provides accurate results for curling during 120 pressurization cycles. The exteroceptive sensor detects the proximity of other robots but shows only a slight correlation during entanglement tests. Finally, we demonstrate that sensorized high-aspect-ratio soft robots can detect the disentanglement of robots under load.
The properties of human skin, such as softness, viscosity, and friction, enable precise handling and manipulation of tools for various tasks. One particularly challenging task in robotic manipulation is operating a knife, where most approaches simplify the problem by rigidly attaching the knife to the robot's end-effector. In this work, inspired by the adaptive nature of human skin, we designed a gripper equipped with four tactile sensors embedded in soft silicone pads to predict the torque applied at the knife. This information is used for closed-loop control during a chopping task. Furthermore, we developed a search algorithm to identify the contact point between the knife and the object, facilitating continuous force estimation throughout the cutting process with an LSTM-based model. Our approach introduces tactile sensing as a novel feedback mechanism for robotic chopping, setting it apart from existing methods that rely on adaptive controllers or vision-based object detection algorithms. The integration of force feed-back, informed by tactile sensors, allows for real-time trajectory and force adjustments, substantially improving the efficiency and precision of the chopping process. Experimental results show that the proposed LSTM-based model can effectively map non-linear and noisy tactile data to estimate contact normal forces, making it highly suitable for closed-loop control in dynamic tasks like cutting.
Wearable sensors for health monitoring have gained significant attention, driving demand for innovative designs and fabrication methods. Recent advancements in flexible piezoresistive conductors have opened up possibilities for more effective wearable technology, enhancing both the precision and comfort of body movement monitoring. Incorporating cellular designs with adjustable and controllable porosity, including triply periodic minimal surface (TPMS) structures, into soft piezoresistive sensors shows great promise. These designs provide controllable deformation mechanisms and improved flexibility, enabling mechanical properties to be tailored for different types of body movements. This study presents TPMS-based soft sensor structures engineered for body bending monitoring, utilizing a cost-effective fabrication approach. Soft sensors were fabricated through fused deposition modeling (FDM) 3D printing, utilizing conductive thermoplastic polyurethane with hyperelastic properties. Two unique TPMS porous structures, I-WP and Gyroid, were incorporated in the design. A finite element simulation according to a hyperelastic model was conducted to simulate the mechanical behavior of these structures, and the findings were compared with experimental results. Furthermore, piezoresistive testing under various bending experiments was conducted. The results demonstrated consistent performance and stability under cyclic loading, establishing these cellular TPMS soft sensors as promising candidates for monitoring body bending movements.
Tails are flexible appendages that many vertebrates use for balance, gait stabilization, thrust generation, and more. While robots rarely have them, soft walking robots may benefit from a tail that stabilizes locomotion in unstructured terrains. We present a tendon-driven, soft robotic tail capable of swinging and shortening to change the momentum and center of mass of a robot body. The tail comprises three origami bellows fully 3D printed from thermoplastic polyurethane. The bellows are highly compressible, allowing motorized tendon actuators to shorten the tail by 110 mm, or 30% of the tail's initial length. The tail's flexibility also enables large swinging motions, which are achieved by alternatively pulling and releasing two tendons routing at the sides of the bellows structures. Since the tail's servo motors are at its end, the tail can generate up to 0.5 N center dot m torque while swinging. In this paper, we characterize the tail's range of adjustable lengths, the swinging motions it can produce, and the torque it generates. Swinging and torque generation are evaluated at several different initial tail lengths. Finally, we demonstrate the tail's controllability through a closed proportional-integral-derivative (PID) feedback controller. This work sets in motion future investigations of how vertebrate-inspired tails can enhance the mobility and stability of (soft) robot walking.
Gas pipeline infrastructure is critical to global energy distribution, but it is prone to frequent, undetected leaks posing risks to the environment and public safety. Current inspection methods are hampered by cost, efficiency, and accuracy, especially in hazardous or inaccessible areas. This paper presents a pneumatically soft robotic solution to enhance gas leak detection through multidimensional locomotion for adaptability to varying environments and high load-bearing capability. The robot uses a compliant scissor linkage for structure, McKibben artificial muscle actuators for locomotion, and magnetic, pouch motor-based grippers to attach to piping. The flexible body enables large deformations to traverse over obstacles and transition between pipeline orientations. In testing, the robot achieves a speed of 15 mm/s when crawling horizontally and 9 mm/s when climbing vertically with a payload of equipment sufficient to support leak detection. This work introduces an efficient platform for infrastructure inspection in a wide array of applications.
Flexible strain sensors are crucial for providing tactile feedback, proprioception, and adaptability in soft robotics, where precise evaluation is especially essential for applications such as biomedical devices. However, traditional sensors face challenges due to their limited flexibility and sensitivity, particularly when deployed on dynamic three-dimensional surfaces. To address these challenges, this work introduces a kirigami-inspired sensor, leveraging the unique kinetic properties of the kirigami structure. This special kirigami called biaxial cutting (BC) pattern significantly enhances the flexibility of 130.8% and sensitivity of 154% while ensuring a high match rate of over 83% across multiple cycles. Additionally, the Kirigami sensor demonstrates exceptional stability (3% error) during the cycle stretching experiments, making it highly suitable for applications requiring continuous deformation. Its excellent improvement hints at its potential as a promising solution for next-generation flexible sensing technologies.
Regulating contact forces is crucial for robotic manipulation, but achieving stiffness variation in multi-fingered hands is challenging due to design complexity. This work presents Mod-VSA, a modular variable stiffness actuator for multi-fingered tendon-driven hands. Mod-VSA consists of concatenated units with a novel stiffness modulation mechanism, allowing a single actuator to adjust stiffness across all units mechanically. The mechanism which can vary the stiffness by a factor of ten, is experimentally characterized with a static force model, and each module includes a Hall sensor for tendon force and displacement measurements. To demonstrate its use-case, the Mod-VSA is combined with the ADAPT Hand, an anthropomorphic multi-fingered hand, to perform finger-level force control and grasping tasks which require both low and high stiffness interactions. Through this VSA design, online force regulation capabilities for a multi-fingered hands can be achieved, expanding their possible applications.
Recently, soft valves have been designed to enhance functionality in soft robots and achieve embodied control. However, most designs still use rigid components, need high control pressure to activate, and are not able to generate high-frequency oscillations. Here, we present a fully-soft valve, which can be triggered by kinking an elastomeric sleeve by using an inflatable origami pouch. The valve is compatible with high supply pressures and can interrupt a flow 100 times larger than its control signal. Afterwards, we use the valve to build fluidic oscillator circuits. These circuits exhibit high frequency oscillations which are tunable on the fly by sliding the valve actuator. We demonstrate the applicability of the circuit by driving a robot that swims at approximate to 0.5 BL.s(-1). Furthermore, we demonstrate that multiple valves can be used to create amplification circuits, able to drive stiffer pneumatic actuators. Such circuits can amplify pressure signals by 10 folds, using only 2 valves.
Robotic harvesting has become a significant topic in recent years as it addresses labor shortages, reduces production costs, and enhances food quality. In this work we present a comprehensive framework for robotic blackberry harvesting. It employs a low-cost 6-DOF robotic arm paired with a soft inflatable gripper specifically designed for blackberries and it leverages the capabilities of YOLOv8 for vision-based control. A standardized set of metrics and tests to objectively evaluate the performance of robotic harvesting in controlled conditions and to inform field deployment is introduced. Our system reached peak performances of 98.4% for the vision component and of 76.6% for grasping effectiveness, with a combined success rate of 52% for the whole pipeline, but with significant variability depending on the pose of the blackberry. The multipurpose arm showed significant limitations, suggesting the development of specialized hardware for future deployment. Our results demonstrate the system’s potential for robotic harvesting of blackberry, while suggesting areas for future research.
Colonoscopy is considered an effective standard method, not only for screening the lower Gastrointestinal (GI), but also for detecting bowel cancer, and removal of tumors. Nevertheless, conventional robotic endoscopes lack maneuvering, leading to discomfort and pain to patients, due to their rigidity and interaction forces with colon’s membrane. Soft colonoscope plays a great role in tackling those problems, owing to their unique maneuverability in tight and curvilinear paths, and safe interaction with constrained environments. However, designing a control approach for such robots is still challenging due to their compliance behavior. This paper presents a compact design of soft cable-actuated robot for colonoscopy. It consists of three subsections, each is 1.5 cm diameter. The middle part acts as a soft spring to provide extensibility during motion, which is embedded within two identical structure subsections. Furthermore, two fabrication technique are explored using traditional 3D printer and laser molding printer to evaluate the durability and response of the design. Finally, Nonlinear Model Predictive Control based on kinematic model is simulated in terms of point stabilization and trajectory tracking while respecting the constraints of real workspace, cable lengths and control actions. The results show that NMPC exhibits superior behavior against avoiding static and dynamic obstacles, while concerning constraints.
