AI based design assistance system for soft robotics -Optimizing complex systems based on the smallest design entity

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In nature, many animals have capabilities that are superior to a rigid robotic structure, despite or because of their compliance. Largely because of their completely new and complementary capabilities compared to conventional hard robots, soft robots form an emerging field of research in robotics. However, to exploit their adaptability, flexibility and compliance, entirely new design and control approaches are needed.The aim of this project is to develop a design assistance system based on profound knowledge of the smallest design entity of a soft robotic system - the pressurizable silicone cylinders of a universal soft bending actuator - to define the optimal design within the multi-parameter space in geometry, actuation and control. The design assistance system will be based on underlying models and analysis of the forward kinematics and dynamics with help of a hierarchical approach of physical models and machine learning algorithms. By just defining the desired path that the universal bending actuator shall fulfill and the loads acting on the structure, the design assistance system shall determine the optimal dimension of all components, as well as the number of actuators connected in series and their actuation. Additional information, as the stress distribution in the bending actuator, the static and dynamic deformation and orientation, and the workspace shall be given as an output. In the first funding period, the focus lies on the numerical generation of appropriate training data for the example of the universal soft bending actuator, the establishment of the design assistance system and the evaluation of different machine learning algorithms, while in the second funding period further general design principals, uncertainties due to fabrication errors, unspecific or actuation dependant parameters, and the control around obstacles shall be incorporated. Even though the underlying physical models and machine learning approaches are adapted to the application of soft robotics, the general approach to build up a modular design assistance system based on the smallest design entity in the system can be transferred to many other design problems.