Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm

The growing interest in soft robotics has resulted in an increased demand for accurate and reliable material modelling. As soft robots experience high deformations, highly nonlinear behavior is possible. Several analytical models that are able to capture this nonlinear behavior have been proposed, however, accurately calibrating them for specific materials and applications can be challenging. Multiple experimental testbeds may be required for material characterization which can be expensive and cumbersome. In this work, we propose an alternative framework for parameter fitting established hyperelastic material models, with the aim of improving their utility in the modelling of soft continuum robots. We define a minimization problem to reduce fitting errors between a soft continuum robot deformed experimentally and its equivalent finite element simulation. The soft material is characterized using four commonly employed hyperelastic material models (Neo Hookean; Mooney–Rivlin; Yeoh; and Ogden). To meet the complexity of the defined problem, we use an evolutionary algorithm to navigate the search space and determine optimal parameters for a selected material model and a specific actuation method, naming this approach as Evolutionary Inverse Material Identification (EIMI). We test the proposed approach with a magnetically actuated soft robot by characterizing two polymers often employed in the field: Dragon Skin™ 10 MEDIUM and Ecoflex™ 00-50. To determine the goodness of the FEM simulation for a specific set of model parameters, we define a function that measures the distance between the mesh of the FEM simulation and the experimental data. Our characterization framework showed an improvement greater than 6% compared to conventional model fitting approaches at different strain ranges based on the benchmark defined. Furthermore, the low variability across the different models obtained using our approach demonstrates reduced dependence on model and strain-range selection, making it well suited to application-specific soft robot modelling.

An obstacle-avoiding and stiffness-tunable modular bionic soft robot

The aim of this work is to design and model a novel modular bionic soft robot for crawling and crossing obstacles. The modular bionic soft robot is composed of several serial driving soft modules, each module is composed of two parallel soft actuators. By analyzing the influence of working pressure and manufacturing size on the stiffness of the modular bionic soft robot, the nonlinear variable stiffness model of the modular bionic soft robot is established. Based on this model, the spatial states and design parameters of the modular bionic soft robot are discussed when the modular bionic soft robot can pass through the obstacle. Experiments show that when the inflation air pressure of the modular bionic soft robot is 70 kPa, its speed can reach 7.89 mm/s and the height of obstacles passed by it can reach 42.8 mm. The feasibility of the proposed modular bionic soft robot and nonlinear variable stiffness model is verified by locomotion experiments.

Soft Robotic Sensing, Proprioception via Cable and Microfluidic Transmission

Current challenges in soft robotics include sensing and state awareness. Modern soft robotic systems require many more sensors than traditional robots to estimate pose and contact forces. Existing soft sensors include resistive, conductive, optical, and capacitive sensing, with each sensor requiring electronic circuitry and connection to a dedicated line to a data acquisition system, creating a rapidly increasing burden as the number of sensors increases. We demonstrate a network of fiber-based displacement sensors to measure robot state (bend, twist, elongation) and two microfluidic pressure sensors to measure overall and local pressures. These passive sensors transmit information from a soft robot to a nearby display assembly, where a digital camera records displacement and pressure data. We present a configuration in which one camera tracks 11 sensors consisting of nine fiber-based displacement sensors and two microfluidic pressure sensors, eliminating the need for an array of electronic sensors throughout the robot. Finally, we present a Cephalopod-chromatophore-inspired color cell pressure sensor. While these techniques can be used in a variety of soft robot devices, we present fiber and fluid sensing on an elastomeric finger. These techniques are widely suitable for state estimation in the soft robotics field and will allow future progress toward robust, low-cost, real-time control of soft robots. This increased state awareness is necessary for robots to interact with humans, potentially the greatest benefit of the emerging soft robotics field.

An Investigation on the Grasping Position Optimization-Based Control for Industrial Soft Robot Manipulator

Mitigating fatigue damage and improving grasping performance are the two main challenging tasks of applying the soft manipulator into industrial production. In this paper, the grasping position optimization-based control strategy is proposed for the soft manipulator and the corresponding characteristics are studied theoretically and experimentally. Specifically, based on the simulation, the resultant stress of step-function-type channels at the same pressure condition that was smallest compared with those of sine-function- and ramp-function-type channels, hence, a pneumatic network with step-function-type channels was selected for the proposed soft manipulator. Furthermore, in order to improve the grasping performance, the kinematics, mechanical, and grasping modeling for the soft manipulator were established, and a control strategy considering the genetic algorithm is introduced to detect the optimal position of the soft manipulator. The corresponding fabrication process and experiments were conducted to cross verify the results of the modeling and the control strategy. It is demonstrated that the internal pressure of the soft manipulator was reduced by 13.05% at the optimal position, which effectively helped mitigate the fatigue damage of the soft manipulator and prolonged the lifespan.

Design and Feasibility Tests of a Gas-driven Bionic Flytrap Soft Robot

The research of bionic soft robot is a complex system engineering, including soft matrix material, soft actuator, soft sensor and bionic control system. Unlike most animals, plants cannot move in whole voluntarily. However, for the purpose of energy and nutrition, various parts of the plant body also carry out various movements, which vary from millisecond to hour on a large time scale. As a result, Plants are considered a source of inspiration for innovative engineering solutions, and a growing number of researchers are investigating the mechanisms of plant movement and biomimetic research. In this paper, the biological morphology, microstructure and movement mechanism of Venus flytrap leaf were studied and analyzed, and a bionic flytrap grassland machine with chamber design was designed and manufactured. Firstly, according to the research report on the biological morphology, microstructure and movement mechanism of Venus flytrap, the idea of chamber design was determined. Based on this observation, we reconstructed the leaf model and bionic structure of Venus flytrap by reverse modeling. Based on the principle of turgor pressure deformation, the chamber design rules of bionic Venus flytrap blade were formulated and optimized with silica gel as the bulk material. The flow channel design of Venus flytrap blade was studied and explored. Finally, the bionic Venus flytrap leaf was made by 3D printing technology and silica gel casting process, and the two bionic leaves were clamped at a certain opening Angle. The bending performance of bionic flytrap blade and the flytrap closure experiment were studied by air pressure excitation. The experimental results show that the bionic Venus flytrap blade can complete bending and closing experiments, and the bionic Venus flytrap can complete the whole capturing process within 5s. The leaf opening Angle of the bionic Venus flytrap reaches 80 degrees, which fits well with the real Venus flytrap blade and meets the design requirements and bionic goals. Apparently, this study is the first to design the chamber of the bionic flytrap leaf, formulate rules, and study the possibility of its deformation. It provides a new idea for the study of the movement and deformation of plant leaves, and expands the application of bionic robots, especially the robot solutions for plant types.

Joseph T. Freeman Award: Creating Synergy in Interdisciplinary Aging Research and Practice

Over 30 years of interdisciplinary practice stimulated many research questions. Early intervention research in sensory impairment, specifically vision and/or hearing impairment was heavily supported by interdisciplinary colleagues from Geriatric Medicine, Nursing, Occupational Therapy, Optometry and Audiology. Challenges and opportunities from this research created a growing interest in developing and designing technologies for older adults. Creating the need for partnerships with engineering. My expertise in aging and access to willing research participants made me an ideal research partner. Effectively expanding my focus beyond sensory impairment interventions to designing and testing robots with older adults. Currently we are testing low cost mobile robots in acute care and the community and are developing and testing a soft robot to assist persons out of a chair as well as turn and lift persons up in bed. The synergy of interdisciplinary practice and research is helping us innovate to improve the lives of older adults.