Increasing gender diversity in engineering using soft robotics

Emerging classes of soft robotics with flexible actuation, intelligent sensibility, and biomimetic functionality are driving significant advances in academic researches and commercial applications. Such new generation robotics relies heavily on important breakthroughs in soft matter engineering and flexible actuation systems. Unlike conventional rigid robots, soft robots generally have unique sporting styles and manufacturing strategies. Recently, a series of newly developed soft materials—gallium‐based liquid metals (LMs) are found to display dramatical roles in making soft machines and robotics with their unusual properties in soft robotic actuation and self‐driven field. With superior merits of both high stretchability and electroconductivity over conventional soft materials, LM is increasingly innovated as flexible sensors and actuators in constructing intelligent soft robots. Typical advances in LM enabled soft machines and robotics are summarized and interpreted, herein, with special focus on the driving principles of the LM objects, the preparation methods of LM flexible electronics for making soft robots, and the applications of LM in biomedicines and intelligent robots, together with the consideration of key challenges facing LM‐based soft robotics and its future prospects.

Educational robotics (ER) is a discipline of applied robotics focused on teaching robot design, analysis, application, and operation. Traditionally, ER has favored rigid robots, overlooking the potential of soft robots (SRs). While rigid robots offer insights into dynamics, kinematics, and control, they have limitations in exploring the depths of mechanical design and material properties. In this regard, SRs present an opportunity to expand educational topics and activities in robotics through their unique bioinspired properties and accessibility. Despite their promise, there is a notable lack of research on SRs as educational tools, limiting the identification of research avenues that could promote their adoption in educational settings. This study conducts a Systematic Literature Review (SLR) to elucidate the impact of SRs across academic levels, pedagogical strategies, prevalent artificial muscles, educational activities, and assessment methods. The findings indicate a significant focus on K-12 workshops utilizing soft pneumatic actuators. Furthermore, SRs have fostered the development of fabrication and mechanical design skills beyond mere programming tasks. However, there is a shortage of studies analyzing their use in higher education or their impact on learning outcomes, suggesting a critical need for comprehensive evaluations to determine their effectiveness, rather than solely relying on surveys for student feedback. Thus, there is an opportunity to explore and evaluate the use of SRs in more advanced settings and multidisciplinary activities, urging for rigorous assessments of their influence on learning outcomes. By undertaking this, we aim to provide a foundation for integrating SRs into the educational robotics curriculum, potentially transforming teaching methodologies and enriching students' learning experiences.

Soft robots have been attracting much research attention in the past decades owing to their unique properties that are inherently different from conventional rigid robots. The intrinsic characteristics of soft robots, such as compliance and flexibility, have made them a promising supplement for many existing robotic applications. For instance, soft robots provide safer human-machine interaction and better adaptability to unexplored environments than the conventional rigid ones. However, their “soft” feature would conversely cause complex material deformation due to their high-DoF nature, which has made their kinematic or dynamic models more difficult to deduce. Logically, the process of designing model-based controllers would therefore suffer from the lack of efficient and accurate modeling methods. In this letter, through adopting Finite Element Method (FEM), we demonstrate a FEM-based nonlinear control method for a soft trunk robot to achieve trajectory tracking objectives. The proposed controller is developed by utilizing the essential information from the dynamic FE model of the studied robot. The details of deriving this nonlinear control method are demonstrated, then followed by 4 practical experiments on the soft truck robot to verify the feasibility of the control method. The corresponding experimental results are presented accordingly.

Soft robots made from soft materials can safely interact with humans and environments compared with the traditional rigid robots. However, the inherent compliance of the soft robots makes them difficult to realize the jumping motion. Although many soft robots can jump through the design of bistable structure. However, the existing soft robots need rigid spine to be embedded in the soft body. Therefore, it is still hard for the jumping of a soft robot with pure soft body. This paper offers the design method for the pure soft bistable robot with jumping ability. The critical buckling force and total elastic potential energy of the robot can be increased by designing the soft body with different thickness. We use the twisted artificial muscle (TAM) to realize the jumping motion of the soft bistable robot. The proposed robot can jump at a height nearly two times its body height. We envision that the design method will promote the application of the soft robot with pure soft body to realize more types of motion strategy.

Soft robots are compliant and durable, and thus potentially better-suited for swimming or crawling over rough terrain than conventional rigid robots. Biomimetic locomotion strategies are often implemented on soft robots, but are difficult to tune because of the complexity of mapping nonlinear mechanical systems to specific gait patterns. We propose an approach to soft robot control in which the robot is designed to physically realize a shape space (mechanically predefined body poses) to follow a theoretically optimal gait. Specifically in this paper we present an entirely soft snake robot designed to implement the prerequisite shape space for slithering gaits. When coupled with a geometric mechanics model relating serpentine shape sequences to displacement, appropriate gait patterns can be selected analytically based on the system's Lie bracket. We developed a modular, two-link soft robot and used the geometric mechanics model to identify several cyclic gait patterns for producing forward displacement. The gait patterns were tested on the soft robot and observed with a motion capture system to measure displacement and monitor shape sequences. Relative performance of the gait patterns is consistent with the geometric “soap-bubble method” as a heuristic for gait optimization, which demonstrates the applicability of this approach to soft robot control and coordination.

Living organisms intertwine soft (e.g., muscle) and hard (e.g., bones) materials, giving them an intrinsic flexibility and resiliency often lacking in conventional rigid robots. The emerging field of soft robotics seeks to harness these same properties to create resilient machines. The nature of soft materials, however, presents considerable challenges to aspects of design, construction, and control—and up until now, the vast majority of gaits for soft robots have been hand-designed through empirical trial-and-error. This article describes an easy-to-assemble tensegrity-based soft robot capable of highly dynamic locomotive gaits and demonstrating structural and behavioral resilience in the face of physical damage. Enabling this is the use of a machine learning algorithm able to discover effective gaits with a minimal number of physical trials. These results lend further credence to soft-robotic approaches that seek to harness the interaction of complex material dynamics to generate a wealth of dynamical behaviors.

In recent years, the field of soft robotics has received considerable attention due to its potential in increasing the safety of human-robot interaction. The design of soft robots possesses great challenges. For example, the longstanding challenge of co-design morphology and actuation makes designing them by hand a trial-and-error process. Earlier work presented by the authors proposes a computational design synthesis (CDS) method for the automated design of virtual, soft locomotion robot morphologies. This work extends the CDS method for morphologies with the automated co-design of actuation. Two methods are considered. In the first method, the actuation of designs is described by parametric actuation curves (PACs) that model feedforward actuation patterns. For every morphology in the design process, a set of PACs is optimized that assumes symmetric and cyclic gaits. The second method, soft actor-critic (SAC) reinforcement learning, removes this assumption as well as models feedback control for comparison. Adding PAC optimization to the CDS method is shown to improve the performance of the resulting designs and to achieve better results within less design iterations. SAC is, however, deemed less effective, due to the need for design specific problem tuning for each new morphology. The SAC experiments also show that the best found soft robot gaits are symmetric and cyclic, although this is not a constraint in the SAC problem formulation, thus verifying the assumptions made in the PAC formulation. To validate the search space modeled in the co-design CDS method, a state-of-the-art soft robot is replicated and compared.

Soft robots can adapt their shape to move through narrow gaps in ways a rigid robot cannot because of their compliance, but most of pneumatic and hydraulic soft robots are still relay on conventional hard valve and it hinder soft robots to completely achieve this. Here we describe a newly quake-typel five-way soft valve. This valve is only 47g and each way of the valve can be controlled individually. When vacuum is applied to the control chamber of the valve, the membranes will be pulled into control chamber and flow can be free to flow. To shape the complicated internal channels of the soft valve, a newly molding technique, which use soluble structure (PVA) as molding cores, was proposed. Compared with the previous soft valves, the soft valve can achieve more complicated function. A starfish-like soft robot was designed and manufactured to illustrate how the soft valve control its bending and stretching.

Although, the compliance of soft robots makes them difficult to control accurately, it also potentially provides an inherently safer solution for coordinated multi-arm manipulation tasks. This is particularly true since deviations in end effector pose do not result in large force buildup in the arms or in the object being manipulated. Coordinated, multi-arm manipulation with soft robots is the goal of the research in this paper. In order to enable this we first develop an evolutionary optimization algorithm to determine the initial placement of each arm for a multi-arm task. We then demonstrate the utility of this optimization in simulation. Lastly, we demonstrate a simple multi-arm control scheme on real soft robots using one soft robot arm, and one rigid robot with low-impedance torque control to manipulate an object through two desired trajectories.

Soft robots, especially soft robotic hands, possess prominent potential for applications in close proximity and direct contact interaction with humans due to their softness and compliant nature. The safety perception of users during interactions with soft robots plays a crucial role in influencing trust, adaptability, and overall interaction outcomes in human-robot interaction (HRI). Although soft robots have been claimed to be safe for over a decade, research addressing the perceived safety of soft robots still needs to be undertaken. The current safety guidelines for rigid robots in HRI are unsuitable for soft robots. In this paper, we highlight the distinctive safety issues associated with soft robots and propose a framework for evaluating the perceived safety in human-soft robot interaction (HSRI). User experiments were conducted, employing a combination of quantitative and qualitative methods, to assess the perceived safety of 15 interactive motions executed by a soft humanoid robotic hand. We analyzed the characteristics of safe interactive motions, the primary factors influencing user safety assessments, and the impact of motion semantic clarity, user technical acceptance, and risk tolerance level on safety perception. Based on the analyzed characteristics, we summarize vital insights to provide valuable guidelines for designing safe, interactive motions in HSRI. The current results may pave the way for developing future soft machines that can safely interact with humans and their surroundings.

Soft robots have become increasingly prevalent in today's AI era due to their ability to meet the specialized needs and technological advancements that rigid robots cannot. Soft robots are characterized by their flexibility, deformability, and variable stiffness, which greatly compensate for the limitations of rigid robots in specialized environments. This chapter aims to provide extensive examples of how soft robots are being applied in various areas including underwater exploration, agriculture, medicine, industry and space. The chapter will discuss the types of soft robots, their movement characteristics, driving methods, and the advantages they bring to different applications. By showcasing the vast potential and research value of soft robots, this chapter underscores their significant role in promoting technological innovation and societal progress.

In the field of robots, soft continuum robots have strong adaptability, and rigid continuum robots have large carrying capacity, but they all have some limitations. The purpose of this paper is to solve the problems of small carrying capacity of soft continuum robots and low safety of rigid continuum robots. Based on the combined action of particle jamming and fiber jamming, we use the good fluidity of spherical particles and the rigidity of spherical particles to realize the flexibility and rigidity of the robot, and put forward the requirement of flexible contact and rigid bearing. According to the large range variable stiffness actual demand, a self-locking soft continuum robot with large range variable stiffness based on the jamming phenomenon is proposed, and the principle of large range variable stiffness is explained. Then the model experiment prototype is designed and manufactured. Based on the model, the combination elastomer is defined. The basic unit is constructed and the theoretical analysis is carried out, and the equivalent of the combination elastomer stiffness model is obtained. Finally, an experimental test system is built. Through the experiment and simulation of variable stiffness, the stiffness change of combination elastomer is obtained, and the specific situation of bearing capacity change is recorded. This study provides a new method to achieve a wide range of stiffness changes, and builds a bridge from soft continuum robots to rigid continuum robots.

The evolution process for creatures is very, very long, and contains many useful secrets and rationality mostly hidden in their structure, motion and configuration. Biomimetic mechatronic design is a useful approach for future mechatronic innovation, which can significantly enhance the performance of mechatronic systems. As an important issue for biomimetic mechatronic design, it is necessary to make a robot as soft as a natural creature to achieve more efficient, high-performance and creature-like motions. Compared to a conventional rigid robot, the design and control of a soft robot is difficult because the coupling between the flexible structure and surrounding environment should be considered, which is very difficult to resolve due to the large deformations and complicated and coupled dynamics. This is the main reason why design methods for soft robots has not been established, despite the many trial developments of soft robots that have been undertaken to date. The challenges for the design of biomimetic soft underwater robots based on numerical simulation considering the coupling between the flexible structure and surrounding fluid as well as control technologies are described in this chapter.

This research paper provides an insight into one of the most promising fields of robotics, which brings together two main elements: the traditional or rigid robotics and the soft robotics. A branch of soft–rigid robots can perform and modulate soft and rigid configurations by means of an approach called jamming. Here we explore how to use layer jamming, namely a set of layers within a flexible membrane, in order to design soft robotics. The paper introduces a quick overview of the history of soft robotics, then it presents the design of a functional prototype of soft–rigid robotic arm with the results of preliminary trials and discussion of future advances where we show the capability of the system in order to lift up possible loads.KeywordsLayer jammingRobotic actuatorsFlexible actuators

The idea of soft robotics has turned out to be one of the most promising areas of modern robotics due to natural flexibility, safety, and naturalistic movement, like natural creatures. The present study will explore the idea of implementing the concept of artificial intelligence (AI) on bio-inspired soft robots to enable morphological adaptation to navigate through adverse environments. The article identifies the advantages of actuator flexibility, compliant mechanisms, and biological influenced body structures that bring enormous dividends in the spheres where the conventional rigid robots would be rendered useless. To be capable of deforming, squeeze, stretch and hold on to objects in a manner that they can navigate through rough surfaces, squeeze in tight crevices and dynamic response to more unpredictable challenges, soft robots are becoming inspired by things like octopuses, caterpillars, worms and snakes. The abstract also explains how the AI techniques, particularly the reinforcement techniques and deep neural networks, and adaptive control algorithms allow robots to adaptively change body shape and stiffness in real-time, increasing their mobility and survivability when exploring or executing rescue missions.Furthermore, the intelligence and data-driven decision-making based on sensors can aid the soft robots to recognize the state of the terrain and modify the movement plans. Their capabilities are enhanced by such capabilities based on AI, which enables them to operate more effectively under such harsh conditions as rubble, mud, in underwater environments, and in fallen buildings. It is emphasized in the study that morphological adaptation is not merely a mechanical ability, but a complex system, which, in turn, presupposes perception, learning, and responsive actuation. The paper concludes that intelligent soft robotics, consisting of bio-inspired design and AI control models, is an innovational solution to the next-generation robotic mobility that can perform its tasks in hazardous, unpredictable, or inaccessible surrounding where traditional robots fail. The work will be added to the evolution of the adaptive soft robotics and the creation of a guideline of the evolution in the sphere of the navigation of the terrain.