Legged robots beyond bioinspiration.

Advances in engineering enable wheeled-legged hybrid locomotion, an achievement not feasible in biological systems.

Why animals can outrun robots.

Animals are much better at running than robots. The difference in performance arises in the important dimensions of agility, range, and robustness. To understand the underlying causes for this performance gap, we compare natural and artificial technologies in the five subsystems critical for running: power, frame, actuation, sensing, and control. With few exceptions, engineering technologies meet or exceed the performance of their biological counterparts. We conclude that biology's advantage over engineering arises from better integration of subsystems, and we identify four fundamental obstacles that roboticists must overcome. Toward this goal, we highlight promising research directions that have outsized potential to help future running robots achieve animal-level performance.

Grasping objects with the aid of haptics.

A smart suction cup uses haptics to supplement vision for exploration of objects in a grasping task.

Human-centered design of a novel soft exosuit for post-stroke gait rehabilitation.

BACKGROUND: Stroke remains a major cause of long-term adult disability in the United States, necessitating the need for effective rehabilitation strategies for post-stroke gait impairments. Despite advancements in post-stroke care, existing rehabilitation often falls short, prompting the development of devices like robots and exoskeletons. However, these technologies often lack crucial input from end-users, such as clinicians, patients, and caregivers, hindering their clinical utility. Employing a human-centered design approach can enhance the design process and address user-specific needs. OBJECTIVE: To establish a proof-of-concept of the human-centered design approach by refining the NewGait® exosuit device for post-stroke gait rehabilitation. METHODS: Using iterative design sprints, the research focused on understanding the perspectives of clinicians, stroke survivors, and caregivers. Two design sprints were conducted, including empathy interviews at the beginning of the design sprint to integrate end-users' insights. After each design sprint, the NewGait device underwent refinements based on emerging issues and recommendations. The final prototype underwent mechanical testing for durability, biomechanical simulation testing for clinical feasibility, and a system usability evaluation, where the new stroke-specific NewGait device was compared with the original NewGait device and a commercial product, Theratogs®. RESULTS: Affinity mapping from the design sprints identified crucial categories for stakeholder adoption, including fit for females, ease of donning and doffing, and usability during barefoot walking. To address these issues, a system redesign was implemented within weeks, incorporating features like a loop-backed neoprene, a novel closure mechanism for the shoulder harness, and a hook-and-loop design for the waist belt. Additional improvements included reconstructing anchors with rigid hook materials and replacing latex elastic bands with non-latex silicone-based bands for enhanced durability. Further, changes to the dorsiflexion anchor were made to allow for barefoot walking. Mechanical testing revealed a remarkable 10-fold increase in durability, enduring 500,000 cycles without notable degradation. Biomechanical simulation established the modularity of the NewGait device and indicated that it could be configured to assist or resist different muscles during walking. Usability testing indicated superior performance of the stroke-specific NewGait device, scoring 84.3 on the system usability scale compared to 62.7 for the original NewGait device and 46.9 for Theratogs. CONCLUSION: This study successfully establishes the proof-of-concept for a human-centered design approach using design sprints to rapidly develop a stroke-specific gait rehabilitation system. Future research should focus on evaluating the clinical efficacy and effectiveness of the NewGait device for post-stroke rehabilitation.

Vision-controlled jetting for composite systems and robots.

Recreating complex structures and functions of natural organisms in a synthetic form is a long-standing goal for humanity1. The aim is to create actuated systems with high spatial resolutions and complex material arrangements that range from elastic to rigid. Traditional manufacturing processes struggle to fabricate such complex systems2. It remains an open challenge to fabricate functional systems automatically and quickly with a wide range of elastic properties, resolutions, and integrated actuation and sensing channels2,3. We propose an inkjet deposition process called vision-controlled jetting that can create complex systems and robots. Hereby, a scanning system captures the three-dimensional print geometry and enables a digital feedback loop, which eliminates the need for mechanical planarizers. This contactless process allows us to use continuously curing chemistries and, therefore, print a broader range of material families and elastic moduli. The advances in material properties are characterized by standardized tests comparing our printed materials to the state-of-the-art. We directly fabricated a wide range of complex high-resolution composite systems and robots: tendon-driven hands, pneumatically actuated walking manipulators, pumps that mimic a heart and metamaterial structures. Our approach provides an automated, scalable, high-throughput process to manufacture high-resolution, functional multimaterial systems.

Glenohumeral joint trajectory tracking for improving the shoulder compliance of the upper limb rehabilitation robot.

BACKGROUND: Exoskeletons have become an important tool to help patients with upper extremity motor dysfunction in rehabilitation training and life assistance. In the study of the upper limb exoskeleton, the human glenohumeral joint will produce accompanying movement during the movement of the shoulder joint. This phenomenon causes a positional deviation between the shoulder joint and the exoskeleton, which affects the accuracy of exoskeleton-assisted human movement and the wearing comfort. Spend. METHOD: Taking the coronal adduction and abduction of the shoulder joint as the research object, the shoulder joint angle and glenohumeral joint bony motion trajectory were fitted by bi-level X-rays, and then the Ultium Motion motion capture system was used to collect the characteristic motion of the shoulder joint surface and establish a model. A back-propagation neural network with shoulder joint motion and shoulder width as input and the coronal position of the glenohumeral joint as output, finally applied the model to the Nimbot exoskeleton upper limb rehabilitation training robot to verify the effectiveness of the algorithm. RESULTS: Real-time prediction of the glenohumeral joint motion trajectory was achieved, and the human-machine coupling compliance during the wearing of the upper limb exoskeleton was improved.

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research.

Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.

[Overview of the Development of Spatial Positioning Accuracy Testing Technology for Surgical Robots].

Characteristics of two major categories of RA equipment which defined in the standard are interpreted firstly. Few representative RA equipment in current market and their key product features are introduced. Then, classifications of different indexes of spatial positioning accuracy are declared, the difficulties of performing testing process on each indexes are further explained. Meanwhile, different kinds of three dimensional coordinate measuring equipment that are cutting edge at present stage are introduced with their main methods of use explained. According to characteristics of three dimensional coordinate measuring equipment on the market, proper measuring equipment for testing certain index of spatial positioning accuracy and corresponding experiment method are introduced.

Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.

OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs. METHODS: A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated. RESULTS: The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%). SIGNIFICANCE: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.

Multi-environment robotic transitions through adaptive morphogenesis.

The current proliferation of mobile robots spans ecological monitoring, warehouse management and extreme environment exploration, to an individual consumer's home1-4. This expanding frontier of applications requires robots to transit multiple environments, a substantial challenge that traditional robot design strategies have not effectively addressed5,6. For example, biomimetic design-copying an animal's morphology, propulsion mechanism and gait-constitutes one approach, but it loses the benefits of engineered materials and mechanisms that can be exploited to surpass animal performance7,8. Other approaches add a unique propulsive mechanism for each environment to the same robot body, which can result in energy-inefficient designs9-11. Overall, predominant robot design strategies favour immutable structures and behaviours, resulting in systems incapable of specializing across environments12,13. Here, to achieve specialized multi-environment locomotion through terrestrial, aquatic and the in-between transition zones, we implemented 'adaptive morphogenesis', a design strategy in which adaptive robot morphology and behaviours are realized through unified structural and actuation systems. Taking inspiration from terrestrial and aquatic turtles, we built a robot that fuses traditional rigid components and soft materials to radically augment the shape of its limbs and shift its gaits for multi-environment locomotion. The interplay of gait, limb shape and the environmental medium revealed vital parameters that govern the robot's cost of transport. The results attest that adaptive morphogenesis is a powerful method to enhance the efficiency of mobile robots encountering unstructured, changing environments.

Adaptive wireless millirobotic locomotion into distal vasculature.

Microcatheters have enabled diverse minimally invasive endovascular operations and notable health benefits compared with open surgeries. However, with tortuous routes far from the arterial puncture site, the distal vascular regions remain challenging for safe catheter access. Therefore, we propose a wireless stent-shaped magnetic soft robot to be deployed, actively navigated, used for medical functions, and retrieved in the example M4 segment of the middle cerebral artery. We investigate shape-adaptively controlled locomotion in phantoms emulating the physiological conditions here, where the lumen diameter shrinks from 1.5 mm to 1 mm, the radius of curvature of the tortuous lumen gets as small as 3 mm, the lumen bifurcation angle goes up to 120°, and the pulsatile flow speed reaches up to 26 cm/s. The robot can also withstand the flow when the magnetic actuation is turned off. These locomotion capabilities are confirmed in porcine arteries ex vivo. Furthermore, variants of the robot could release the tissue plasminogen activator on-demand locally for thrombolysis and function as flow diverters, initiating promising therapies towards acute ischemic stroke, aneurysm, arteriovenous malformation, dural arteriovenous fistulas, and brain tumors. These functions should facilitate the robot's usage in new distal endovascular operations.

Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference.

Robotic harvesting research has seen significant achievements in the past decade, with breakthroughs being made in machine vision, robot manipulation, autonomous navigation and mapping. However, the missing capability of obstacle handling during the grasping process has severely reduced harvest success rate and limited the overall performance of robotic harvesting. This work focuses on leaf interference caused slip detection and handling, where solutions to robotic grasping in an unstructured environment are proposed. Through analysis of the motion and force of fruit grasping under leaf interference, the connection between object slip caused by leaf interference and inadequate harvest performance is identified for the first time in the literature. A learning-based perception and manipulation method is proposed to detect slip that causes problematic grasps of objects, allowing the robot to implement timely reaction. Our results indicate that the proposed algorithm detects grasp slip with an accuracy of 94%. The proposed sensing-based manipulation demonstrated great potential in robotic fruit harvesting, and could be extended to other pick-place applications.

IoT Security and Computation Management on a Multi-Robot System for Rescue Operations Based on a Cloud Framework.

There is a growing body of literature that recognizes the importance of Multi-Robot coordination and Modular Robotics. This work evaluates the secure coordination of an Unmanned Aerial Vehicle (UAV) via a drone simulation in Unity and an Unmanned Ground Vehicle (UGV) as a rover. Each robot is equipped with sensors to gather information to send to a cloud server where all computations are performed. Each vehicle is registered by blockchain ledger-based network security. In addition to these, relevant information and alerts are displayed on a website for the users. The usage of UAV-UGV cooperation allows for autonomous surveillance due to the high vantage field of view. Furthermore, the usage of cloud computation lowers the cost of microcontrollers by reducing their complexity. Lastly, blockchain technology mitigates the security issues related to adversarial or malicious robotic nodes connecting to the cluster and not agreeing to privacy rules and norms.

InjectMeAI-Software Module of an Autonomous Injection Humanoid.

The recent pandemic outbreak proved social distancing effective in helping curb the spread of SARS-CoV-2 variants along with the wearing of masks and hand gloves in hospitals and assisted living environments. Health delivery personnel having undergone training regarding the handling of patients suffering from Corona infection have been stretched. Administering injections involves unavoidable person to person contact. In this circumstance, the spread of bodily fluids and consequently the Coronavirus become eminent, leading to an upsurge of infection rates among nurses and doctors. This makes enforced home office practices and telepresence through humanoid robots a viable alternative. In providing assistance to further reduce contact with patients during vaccinations, a software module has been designed, developed, and implemented on a Pepper robot that estimates the pose of a patient, identifies an injection spot, and raises an arm to deliver the vaccine dose on a bare shoulder. Implementation was done using the QiSDK in an android integrated development environment with a custom Python wrapper. Tests carried out yielded positive results in under 60 s with an 80% success rate, and exposed some ambient lighting discrepancies. These discrepancies can be solved in the near future, paving a new way for humans to get vaccinated.

Running birds reveal secrets for legged robot design.

Recapitulating avian locomotion opens the door for simple and economical control of legged robots without sensory feedback systems.

Electronics-free soft robot has a nice ring to it.

A fluidic ring oscillator provides timing to soft robots, thus enabling complex locomotion and load carrying.

A miniature robotic steerable endoscope for maxillary sinus surgery called PliENT.

In endoscopic maxillary sinus surgery, the maxillary sinus is accessed through the nasal cavity which constitutes a narrow and tortuous pathway. However, surgeons still use rigid endoscopes and rigid, straight or pre-bent instruments for this procedure. Resection of the uncinate process and creation of a medial antrostomy is warranted to access the pathology inside the maxillary sinus and depending on the location of the pathology (lateral, inferior or anterior wall), additional resection of healthy tissue and/or functional structures like the lacrimal duct and/or inferior turbinate is necessary to gain optimal access. In order to avoid this additional resection, a functional single-handed, steerable endoscope for endoscopic maxillary sinus surgery has been designed and built. This endoscope is, to our knowledge, the most slender active steerable endoscope ever reported for maxillary sinus surgery. The performance of the endoscope was validated by two surgeons on a cadaver. An increased field of view was found in comparison to currently used endoscopes. As a direct consequence, a reduced need for resection of healthy tissue was confirmed.

3D printing of resilient biogels for omnidirectional and exteroceptive soft actuators.

Soft robotics greatly benefits from nature as a source of inspiration, introducing innate means of safe interaction between robotic appliances and living organisms. In contrast, the materials involved are often nonbiodegradable or stem from nonrenewable resources, contributing to an ever-growing environmental footprint. Furthermore, conventional manufacturing methods, such as mold casting, are not suitable for replicating or imitating the complexity of nature's creations. Consequently, the inclusion of sustainability concepts alongside the development of new fabrication procedures is required. We report a customized 3D-printing process based on fused deposition modeling, printing a fully biodegradable gelatin-based hydrogel (biogel) ink into dimensionally stable, complex objects. This process enables fast and cost-effective prototyping of resilient, soft robotic applications from gels that stretch to six times their original length, as well as an accessible recycling procedure with zero waste. We present printed pneumatic actuators performing omnidirectional movement at fast response times (less than a second), featuring integrated 3D-printed stretchable waveguides, capable of both proprio- and exteroception. These soft devices are endowed with dynamic real-time control capable of automated search-and-wipe routines to detect and remove obstacles. They can be reprinted several times or disposed of hazard-free at the end of their lifetime, potentially unlocking a sustainable future for soft robotics.

Harnessing the circular economy to develop sustainable soft robots.

Fully recyclable and degradable materials have been used for the development of soft devices for omnidirectional sensing and actuation.

Bioinspired elastomer composites with programmed mechanical and electrical anisotropies.

Concepts that draw inspiration from soft biological tissues have enabled significant advances in creating artificial materials for a range of applications, such as dry adhesives, tissue engineering, biointegrated electronics, artificial muscles, and soft robots. Many biological tissues, represented by muscles, exhibit directionally dependent mechanical and electrical properties. However, equipping synthetic materials with tissue-like mechanical and electrical anisotropies remains challenging. Here, we present the bioinspired concepts, design principles, numerical modeling, and experimental demonstrations of soft elastomer composites with programmed mechanical and electrical anisotropies, as well as their integrations with active functionalities. Mechanically assembled, 3D structures of polyimide serve as skeletons to offer anisotropic, nonlinear mechanical properties, and crumpled conductive surfaces provide anisotropic electrical properties, which can be used to construct bioelectronic devices. Finite element analyses quantitatively capture the key aspects that govern mechanical anisotropies of elastomer composites, providing a powerful design tool. Incorporation of 3D skeletons of thermally responsive polycaprolactone into elastomer composites allows development of an active artificial material that can mimic adaptive mechanical behaviors of skeleton muscles at relaxation and contraction states. Furthermore, the fabrication process of anisotropic elastomer composites is compatible with dielectric elastomer actuators, indicating potential applications in humanoid artificial muscles and soft robots.