Check out our new paper on a foam based soft actuator and the hybrid actuation system at IROS 2020. We also contributed a poster to the workshop Application-Driven Soft Robotic Systems: Translational Challenges and won the Ocado Technology prize in the industrial application category!
Soft material robots offer a number of advantages over traditional rigid robots in applications including human-robot interaction, rehabilitation and surgery. These robots can navigate around obstacles, elongate, squeeze through narrow openings or be squeezed – and they are considered to be inherently safe. The ability to stiffen compliant soft actuators has been achieved by embedding various mechanisms that are generally decoupled from the actuation principle. Miniaturisation becomes challenging due to space limitations which can in turn result in diminution of stiffening effects. Here, we propose to hydraulically actuate soft manipulators with low-melting-point materials and, at the same time, be able to vary their stiffness. Instead of allocating an additional stiffening chamber within the soft robot, one chamber only is used for actuation and stiffening. Low Melting Point Alloy is integrated into the actuation chamber of a single-compartment soft robotic manipulator and the interfaced robotic syringe pump. Temperature change is enabled through embedded nichrome wires. Our experimental results show higher stiffness factors, from 9−12 opposing the motion of curvature, than those previously found for jamming mechanisms incorporated in separate additional chambers, in the range of 2−8 for the same motion.
My Colleague and me had a great time at IROS 2019 in Macao. So many interesting people and research topics in just one week and one place!
SpineMan is designed as a prototype of a soft robotic manipulator that is constructed of alternating hard and soft segments similar to the human spine. Implementing such soft segments allows to surpass the rigidity of conventional robots and ensures safer workspaces where humans and machines can work side by side with less stringent safety restrictions. Therefore, we used a hydrogel as viscoelastic material consisting of poly (vinyl alcohol) and borax. The mechanical properties of the hydrogel were tailored by embedding silica particles of various particles sizes as well as in different mass fractions. Increased mass contents as well as larger particle sizes led to strongly enhanced rigidity with a more than doubled storage modulus of the composite compared to the pure hydrogel. Furthermore, specific functionalities were induced by the incorporation of superparamagnetic Fe3O4 nanoparticles that can in principle be used for sensing robotic motion and detecting malfunctions. Therefore, we precisely adjusted the saturation magnetization of the soft segments using defined mass contents of the nanoparticles. To ensure long-time shape stability and prevention of atmospheric influences on the prepared composites, a silicone skin of specific shore hardness was used. The composites and the soft segments were characterized by oscillation measurements, cryo-SEM, bending tests and SQUID measurements, which give insights into the properties in the passive and in the moving state of SpineMan. The utilization of tailored composites led to highly flexible, reinforced and functional soft segments, which ensure stability, easy movability by springs of the shape memory alloy nitinol and prevention of total failure.
(Preller, T.; Runge, G.; Zellmer, S.; Menzel, D.; Saein, S.; Peters, J.; Raatz, A.; Tiersch, B.; Koetz, J.; Garnweitner, G. (2019). Particle-reinforced and functionalized hydrogels for SpineMan, a soft robotics application. Journal of Materials Science. 54. 10.1007/s10853-018-3106-6. )
Match/IFA | The project SafeMate focuses on general implementation strategies for an accepted and safe human-robot collaboration. A team of researchers will present the research work using an example of the assembly of a washing machine at automatica 2018 in Munich from 19 to 22 June.
Within the scope of the project SafeMate, the Institute of Production Systems and Logistics (IFA) and the Institute of Assembly Technology (match) is doing research in cooperation with nine industrial partners general on implementation strategies of collaborative assembly systems. The researchers’ aim is to prepare guidelines that help companies to identify which workplaces are suitable for human-robot collaboration (HRC) and how to get the planning started.
At automatica taking place in Munich from 19 to 22 June 2018, the researchers will present an application in the field of washing machine assembly: Assembling an air trap, a metering part in the washing machine, proved to be particularly difficult. Therefore, a robot will carry out this assembly step in the future. At the joint stand of the Wissenschaftliche Gesellschaft für Montage, Handhabung und Industrierobotik (WGMHI), further projects related to HRC and Industry 4.0 will be presented to an interested expert audience.
[phi, https://www.phi-hannover.de/startseite/artikel/detail/human-robot-collaboration-at-automatica-2018/ ]
The ideas for the designs of soft robots are often derived from nature and reduced to simple shapes for realization. Today, however, the design is often not yet systematically optimized and, compared to classical robotics, only a few tools for the design of soft robots are available. The aim of my thesis is to implement an optimization algorithm that further develop a found kinematics of a soft robot in order to find an optimal design without material and manufacturing effort.
I used a genetic algorithm in MATLAB to optimize the design of two different pneumatic actuators regarding specific factors like maximizing the bending and, at the same time, keep the ballooning as low as possible. Therefor a group (generation) of actuators (individuals) with different designs is evaluated in Abaqus FEA, compared to each other and then selected to get into the next generation. The design parameters of the choosen individuals are changed (mutated) and recombined before the start of the next optimization cycle.