Small-scale soft-bodied robot with multimodal locomotion.

Soft robots equipped with multifunctionalities have been increasingly needed for secure, adaptive, and autonomous functioning in unknown and unpredictable environments. Robotic stacking is a promising solution to increase the functional diversity of soft robots, which are required for safe human-machine interactions and adapting in unstructured environments. However, most existing multifunctional soft robots have a limited number of functions or have not fully shown the superiority of the robotic stacking method. In this study, we present a novel robotic stacking strategy, Netting-Rolling-Splicing (NRS) stacking, based on a dimensional raising method via 2D-to-3D rolling-and-splicing of netted stackable pneumatic artificial muscles to quickly and efficiently fabricate multifunctional soft robots based on the same, simple, and cost-effective elements. To demonstrate it, we developed a TriUnit robot that can crawl 0.46 ± 0.022 body length per second (BL/s) and climb 0.11 BL/s, and can carry a 3 kg payload while climbing. Also, the TriUnit can be used to achieve novel omnidirectional pipe climbing including rotating climbing, and conduct bionic swallowing-and-regurgitating, multi-degree-of-freedom manipulation based on their multimodal combinations. Apart from these, steady rolling, with a speed of 0.19 BL/s, can be achieved by using a pentagon unit. Furthermore, we applied the TriUnit pipe climbing robot in panoramic shooting and cargo transferring to demonstrate the robot's adaptability for different tasks. The NRS stacking-driven soft robot here has demonstrated the best overall performance among existing stackable soft robots, representing a new and effective way for building multifunctional and multimodal soft robots in a cost-effective and efficient way.

Inchworm‐like robots have been increasingly investigated in recent years. However, most studies have focused on one or several locomotion modes, lacking in terms of inverted climbing, movement with a heavy load, and climbing a vertical plane. Herein, an inchworm‐like soft robot is actuated with multimodal locomotion using an acrylic stick‐constrained dielectric elastomer actuator (ASCDEA). By assembling the ASCDEA with a flexible support frame, a soft saddle‐like shape body is designed to provide the shape deformation ability for the robot. This design enables the robot to move under a heavy load. Two electroadhesion actuators are designed to implement the anchoring action. Additionally, a coordination control strategy is developed to synchronize the shape deformation and electroadhesion for multimodal locomotion. Experimental results confirm that the designed robot can move at a maximum velocity of 39.55 mm s−1 (0.53 body length s−1) on an inverted plane. Moreover, the robot is capable of multimodal locomotion, including inverted climbing, vertical climbing, horizontal crawling, turning locomotion, and rapid movement with a heavy load. The developed soft robot can navigate through a confined horizontal tunnel while carrying a payload, cross a gap, circumvent obstacles, and is particularly robust owing to its compliance.

Earthworms have entirely soft bodies mainly composed of circular and longitudinal muscle bundles but can handle the complexity of unstructured environments with exceptional multifunctionality. Soft robots are naturally appropriate for mimicking soft animal structures thanks to their inherent compliance. Here, we explore the new possibility of using this compliance to coordinate the actuation movements of single-type soft actuators for not only high adaptability but the simultaneous multifunctionality of soft robots. A cross-linked actuator coordination mechanism is proposed and explained with a novel conceptual design of a cross-linked network, characterization of modular coordinated kinematics, and a modular control strategy for multiple functions. We model and analyze the motion patterns for these functions, including grabbing, manipulation, and locomotion. This further enables the combination of simultaneous multi-functions with this very simple actuator network structure. In this way, a soft modular robot is developed with demonstrations of a novel continuous-transportation mode, for which multiple objects could be simultaneously transported in unstructured environments with either mobile manipulation or pick-and-place operation. A comprehensive workflow is presented to elaborate the cross-linked actuator coordination concept, analytical modeling, modular control strategy, experimental validation, and multi-functional applications. Our understanding of actuator coordination inspires new soft robotic designs for wider robotic applications.

Energy in Robotics: An Interdisciplinary Challenge

Flexible discretely-magnetized configurable soft robots via laser-tuned selective transfer printing of anisotropic ferromagnetic cells

The small-scale robot using soft materials features extraordinary compliance and a large amount of degree of freedoms, but its nonlinearity in material increases the system complexity for control strategy. In this paper, as inspired by the skiing-sport, a lightweight locomotion robot with a rigid poking pole is designed and driven by an artificial muscle, a conical dielectric elastomer actuator (DEA). The poking mechanism transmits the stroke of DEA into robot motion, contributing the material compliance to the capability of terrain adaptivity in the robot. The combination of a soft actuator and a rigid transmission mechanism lowers the complexity in the robotic kinetics and increases the loading capability for device carrying. Experiments verify the locomotion on the dimple and wavy pavement as well as clambering a slope. This robot offers performances including: (i) a maximum speed of 64.1 mm (0.43 body length) per second; (ii) a loading capability of 7.7 times of its weight; (iii) a steering angle of 25°at a radius of 18 cm.

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time, which would have deteriorating effects on the robot motion and task performance. Therefore, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme to find controller parameters while optimizing the stride length performance of the walking soft millirobot robot within a small number of physical experiments. We demonstrate adaptation to fabrication variabilities in three different robots and to walking surfaces with different roughness. We also show an improvement in the learning performance by transferring the learning results of one robot to the others as prior information.

Robots have been around us for quite some time now. Robots are machines capable of performing tasks on their own with high accuracy and precision and minimal supervision to reduce human effort. Our daily lives are touched by different forms of robots around us, be it a direct interaction in form of robotic helpers at the airport or indirectly in the case of industrial arm robots assembling the electronics and machines. Traditionally, there are several component systems to robotics and one of the prominent one is actuation, which is made from hard and rigid components in these conventional robots. However, this is responsible for their intermittent motion. A new field of research, soft robotics, makes of soft materials for their fabrication and draws inspiration from natural organisms and how they produce fluidic and complex motions. There are different ways by which these soft materials can be actuated and include techniques like applying pneumatic and hydraulic pressure, applying heat, making use of a chemical process like combustion, applying varying magnetic fields and applying electric fields. Among these, the class of actuators employing soft materials deformed by applying the electric field, known as Dielectric elastomer actuators, are preferred owing to their advantages of simple fabrication, large actuation strain and control by electrical stimulus. The architecture of these actuators resembles a variable capacitor with electrodes on either sides of the elastomer, and their performance depends on material properties like dielectric constant and mechanical stiffness. Generally, researchers have tried to modify either of these properties with addition of solid conductive or ceramic fillers, or chemical modification of the elastomer, and this has led to undesirable changes on the other material properties. Drawing inspiration from biological materials like skin, which is a soft material system filled with fluids, a novel composite is synthesized utilizing a solid polymer matrix and a high dielectric constant liquid filler, an ionic liquid. An increase in dielectric constant is observed accompanied with significant reduction in mechanical stiffness. The novel composites show significant improvement upon the actuation performance of the commonly used elastomeric system at considerably low electric fields. Owing to the judicious choice of material systems, the composites also show high stability and transparency. With emergence of new generation technologies such as display touchscreens, touchpads, virtual reality and augmented reality, human machine interaction and haptics has become ever more important. Soft actuators and dielectric elastomer actuators (in particular), owing to their inherent softness, have convincing advantage over the conventional rigid actuators. Additionally, state-of-the-art technologies for haptics primarily seek to simulate the perception of texture change with sensory manipulation. Furthermore, dielectric elastomer actuators in their current form suffer from visible obstructiveness and non-integrable architecture. A new device architecture for dielectric elastomer actuators, capable of producing on-demand surface texture change and local topographic features, is demonstrated. Owing to its unique architecture, a highly transparent, flexible and integrable configuration of the device is showed. Out-of-plane deformations and force feedback from the devices are shown to be well above the perceptual threshold of sensors, known as mechanoreceptors, present in our fingertips. Another attribute of the biological appendages in natural organisms is the ability to reversibly modulate its mechanical properties upon demand, which still eludes the field of soft robotics. One of the best examples of this is found in human body, where muscles modulate their stiffness in accordance to the load distribution and movement. Most of the current approaches mange to only demonstrate reversible stiffness and involves combination of a passive reversible stiffness component with the existing actuator. Hence, a novel low-temperature phase change material is identified for enabling reversible rigidity in the soft actuators owing to its low melting point, significant gap between melting and solidification, superior thermal stability, high mechanical strength and high dielectric constant. Thermal control is chosen the modulation of mechanical properties, owing to its lightweight design and scalability. The fabricated reversible rigidity composites show impressive variations in stretchability, bendability and hardness. Owing to its ability to melt and re-solidify, the composites also demonstrate a healing behaviour with the ability to regain complete structural integrity and mechanical strength. These novel ultra-soft, high-k liquid filler-based polymer composites, reversible rigidity composites and transparent and integrable device architectures for field-driven soft actuators could pave the way for next generation applications such as shape morphing, adaptive surfaces and transparent haptic devices.

My new colleague has artificial muscles: a DEA based approach for inherently compliant robotic systems

Soft robots with high degree of compliance can easily deform to delicate task settings and unstructured environments, realizing more adaptive and comfort interfaces for human-machine and human-environment. Since soft robots are made with materials with moduli similar to soft biological matters and capable of autonomous movements, scientists have been seeking insights from the nature due the excellent structural and functional capabilities. Most of the design inspiration for soft robots come from animals, such as cephalopod mollusks, because they have no rigid body structure and yet, they can perform sophisticated tasks and move around swiftly. However, very limited studies on plant-based soft robots are present, because as compared to animals, plants do not possess mobility and fast interactions with environmental changes.

Bio-inspired robotic structure composed of soft actuation units has attracted increasing research interests in its potential and capacity of complying with unstructured and dynamic environment, as well as providing safe interaction with human; however, this inevitably poses technical challenging to achieve steady, reliable control due to the remarkable non-linearity of its kinematics and dynamics. To resolve this challenge, we propose a novel control framework that can characterize the kinematics of a soft continuum robot through the hyper-elastic Finite-element modeling (FEM). This enables frequent updates of the Jacobian mapping from the user motion input to the end-effector's point of view. Experimental validation has been conducted to show the feasibility of controlling the soft robot for intracavitary path following. This could be the first success to demonstrate the perspectives of achieving stable, accurate and effective manipulation under large change of robot morphology without having to deduce its analytical model. It is anticipated to draw further extensive attention on resolving the bottleneck against the application of FEM, namely its intensive computation.

Soft robotics, intended as the use of soft materials in robotics, is a young yet promising and growing research field. The need for soft robots emerged in robotics, for facing unstructured environments, and in artificial intelligence, too, for implementing the embodied intelligence, or morphological computation, paradigm, which attributes a stronger role to the bodyware and its interaction with the environment. Using soft materials for building robots poses new technological challenges: the technologies for actuating soft materials, for embedding sensors into soft robot parts, for controlling soft robots are among the main ones. Though still in its early stages of development, soft robotics is finding its way in a variety of applications, where safe contact is a main issue, in the biomedical field, as well as in exploration tasks and in the manufacturing industry. Literature in soft robotics is increasingly rich, though scattered in many disciplines. The soft robotics community is growing worldwide and initiatives are being taken, at international level, for consolidating this community and strengthening its potential for disruptive innovation.

In narrow, unstructured underwater environments such as target monitoring and minimally invasive medical procedures, micro soft robots exhibit unique advantages due to their flexible movement capabilities and small size. At the same time, bionic design of a micro soft robot, can significantly improve their swimming performance. However, limited by their small size, these robots are difficult to power internally and usually adopt a wireless power supply design. This study designs and fabricates a magnetically responsive, bionic micro soft robot based on the swimming principle of the cownose ray. The robot is made of a mixture of neodymium iron boron (NdFeB) and polydimethylsiloxane in a certain proportion. Then, a three-dimensional Helmholtz coil is used to generate an oscillating harmonic magnetic field, allowing for swimming experiments on the robot to explore the influence of magnetic field parameters on its swimming performance. The experimental results show that the swimming speed is fastest atB= 5 mT andf= 11 Hz, reaching 5.25 mm s-1, about 0.5 body lengths per second. Additionally, by adjusting the current direction and frequency of the coil, the robot can execute various swimming modes, including straight swimming, turning swimming, and directional swimming. By employing a stepwise adjustment method, the impact of response errors on the robot's trajectory can be effectively reduced. This study demonstrates the feasibility of a magnetically driven micro soft robot, laying the foundation for the application of wirelessly driven robots in underwater narrow spaces.

Mechanoreception, the ability of robots to detect mechanical stimuli from the internal and external environments, contributes significantly to improving safety and task performance during the operation of robots in unstructured environments. Various approaches have been proposed to endow robot systems with mechanoreception. In the case of soft robots, the state-of-the-art mechanosensory solutions typically embedded dedicated deformable sensors into the soft body, giving rise to fabrication complexity and signal sophistication. In this study, we propose a novel mechanoreception scheme to enable pneumatic-driven soft robots to perceive proprioceptive movements as well as external contacts. Both internal and external mechanical parameters can be decoded from intuitive cues of body deformation and pneumatic pressure signals. In contrast to most existing solutions employing dedicated deformable sensors, the proposed approach only utilizes pressure feedback, which is typically available from the pneumatic pressure sensors incorporated in the control loop of most pneumatic soft robots. The concept was implemented and validated on a proprietary robotic gripper with a linear soft pneumatic actuator, demonstrating the capability in simultaneous detection of actuator position and external contact forceAfter the proposed approach, the gripper can achieve both active and passive mechanosensation, with demonstrated experiments in grasping force estimation, contact loss detection, object stiffness identification, and contour measurements. This approach offers an alternative route to achieving excellent internal/environmental awareness without requiring dedicated sensing modalities.

Soft robotics has attracted growing attention due to its compliant structure, safe operation, and promising applications in constrained and unstructured environments. Octopus can employ its arm and suckers to catch prey in complex environments. Intrigued by these capabilities, we explored a new approach in sensing, modeling, and control of a bioinspired soft robot that enables both bending and suction for manipulating and perception of environmental load. By implementing EGaIn-based soft sensors with a crosswise manner on the soft tentacle, both sensory feedbacks of the elongation and expansion induced by the pneumatic inflation can be acquired. In this article, we built a variable curvature model for the bending kinematic reconstruction of the soft robot based on the feedback of the soft sensors. Both modeling and experimental results show that this crosswise sensor design allows for more accurate sensing of the soft robot's bending than the one-axis design. With the crosswise sensor, the robotic tentacle can distinguish the bending under external stimuli and internal self-actuation. We experimentally verified the sensory response as well as the accuracy of the self-sensing system and the model under freeload bending, externally loaded bending, and grasping conditions. By synergistically adopting the sensing of the vacuum-actuated suckers with the bending arm, we show that the soft robot can detect, suck, and bend to grasp objects. The results from this article may provide new design and application insights into the creation of autonomous soft manipulators with both bending and suction for tasks in constrained environments.