Generative Shape Optimization of Soft Pneumatic Actuators ... Generative Shape Optimization of Soft

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  • Generative Shape Optimization of Soft Pneumatic Actuators Using

    Cloth-Simulation

    Using Various Parameterizations to Generatively Optimize Thin-Walled Soft

    Pneumatic Actuators

    Henrik Solvang Holmsen

    Thesis submitted for the degree of Master in Robotics and Intelligent Systems

    60 credits

    Department of Informatics Faculty of mathematics and natural sciences

    UNIVERSITY OF OSLO

    Spring 2019

  • Generative Shape Optimization of Soft Pneumatic Actuators Using

    Cloth-Simulation

    Using Various Parameterizations to Generatively Optimize Thin-Walled Soft

    Pneumatic Actuators

    Henrik Solvang Holmsen

  • © 2019 Henrik Solvang Holmsen

    Generative Shape Optimization of Soft Pneumatic Actuators Using Cloth-Simulation

    http://www.duo.uio.no/

    Printed: Reprosentralen, University of Oslo

  • Abstract

    Soft robotics has shown a lot of great promise in recent years. However, most of the soft muscles/actuators used today often rely on designs developed in the 1950s and requires the use of different materials to achieve actuation. In this thesis the generative design paradigm was used to design such soft inflatable actuators, using a single thickness, and a single material.

    This thesis, in particular, focuses on optimizing the surface shape of such actuators, in order to make them contract as much as possible when inflated. A variety of different search spaces were explored, first by using a basic sine function. An optimal amplitude was found, and further results in these experiments indicated that several actuators in series were beneficial for contraction.

    Evolutionary algorithms were also used to find an optimal number, and magnitude of b-spline control points, to design a soft actuator for optimal contraction. In the simulation, an optimal shape was found, with a fitness exceeding that of the sine-function based actuators.

    Then these optimal actuators were tested, with external forces applied to both sides, simulating stretch. It was found that the area between the individual vertices in the mesh greatly impacted the stretch of each actuator and that this test not necessarily gave a good indication of the strength. However, by visual inspection some useful conjecture could be derived, in that, the more uniform shapes seemed to stretch less than the more complex shapes.

    i

  • Acknowledgements

    First of all, I want to thank my supervisor, associate professor Mats Erling Høvin for support during the work of this thesis and for helpful discussions when both creating the simulation environment and finding good parameterizations of the meshes. Secondly, I would like to thank my fellow students for interesting input when creating this thesis. Lastly, I want to thank my friends and family for their support.

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  • Contents

    I Introduction and Background 1

    1 Introduction 2 1.1 Research Goals . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

    2 Background 6 2.1 Generative Design . . . . . . . . . . . . . . . . . . . . . . . . 6

    2.1.1 Soft Body Simulation . . . . . . . . . . . . . . . . . . . 7 2.1.2 Local Search . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.3 Generative/Evolutionary Design Challenges . . . . . 16

    2.2 Soft Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.1 Actuation . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.2 Recent work in Generative Design of Robots, And

    Soft Robot Design . . . . . . . . . . . . . . . . . . . . . 18

    II The Project 22

    3 Tools and Implementation 23 3.1 Software and Tools . . . . . . . . . . . . . . . . . . . . . . . . 23

    3.1.1 Open Frameworks . . . . . . . . . . . . . . . . . . . . 23 3.1.2 DEAP . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

    3.2 Implementation and setup . . . . . . . . . . . . . . . . . . . . 24 3.2.1 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2.2 Fitness . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.2.3 Limiting the Search Space . . . . . . . . . . . . . . . . 33 3.2.4 Exhaustive Search, Method, and Implementation . . 34 3.2.5 Evolutionary Algorithm, Method and Implementation 36

    III Expermients and Results 42

    4 Sinusoids 43 4.1 Variable Amplitude Search . . . . . . . . . . . . . . . . . . . . 43

    4.1.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

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  • 4.1.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2 Variable Amplitude and Range . . . . . . . . . . . . . . . . . 46

    4.2.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.2.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 47

    4.3 Variable Amplitude and Range With Absolute Function . . . 49 4.3.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.3.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 52

    5 B-splines and Evolutionary Optimization 53 5.1 Single B-spline Over Entire Mesh . . . . . . . . . . . . . . . . 54

    5.1.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.1.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 55

    5.2 Single B-spline Over the Middle Mesh . . . . . . . . . . . . . 57 5.2.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.2.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 59

    5.3 Two B-splines Over the Middle Mesh . . . . . . . . . . . . . 60 5.3.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.3.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 62

    5.4 Four B-splines Over the Middle Mesh . . . . . . . . . . . . . 64 5.4.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 5.4.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 65

    5.5 Random X and Y Coordinates . . . . . . . . . . . . . . . . . . 68 5.5.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.5.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 69

    6 Testing The Optimal Actuators 71 6.1 Optimal Sinusoids . . . . . . . . . . . . . . . . . . . . . . . . 71

    6.1.1 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.2 Optimal B-splines . . . . . . . . . . . . . . . . . . . . . . . . . 73

    6.2.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.2.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 74

    IV Conclusion and Discussion 76

    7 Discussion 77 7.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 7.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

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  • List of Figures

    2.1 Spring mass simulator setup . . . . . . . . . . . . . . . . . . . 8 2.2 Vector product used for inflation . . . . . . . . . . . . . . . . 10 2.3 Flowchart showing how a EA is run . . . . . . . . . . . . . . 12 2.4 Visualization of a two point crossover . . . . . . . . . . . . . 14 2.5 Design and concept of a pleated PAM . . . . . . . . . . . . . 18 2.6 Evolved and manufactured soft robot . . . . . . . . . . . . . 19 2.7 Evolving robot gaits using VoxCAD . . . . . . . . . . . . . . 19 2.8 Evolutionary optimization in bending FEA . . . . . . . . . . 20 2.9 Figure of soft robotic fish . . . . . . . . . . . . . . . . . . . . . 21

    3.1 Visualization of the internal mesh structure . . . . . . . . . . 25 3.2 Visalization of internal forces . . . . . . . . . . . . . . . . . . 26 3.3 Visalization of how where the internal forces are added in

    the mesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4 Flowchart for one run in the simulation . . . . . . . . . . . . 28 3.5 UML of the simulator data structure . . . . . . . . . . . . . . 30 3.6 Figure of the fitness calculation . . . . . . . . . . . . . . . . . 32 3.7 Sine-functions applied to the base-cylinder . . . . . . . . . . 35 3.8 Flowchart of how the evaluation of different sinusoids are

    set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.9 How b-splines are impacted by the control-point placement 37 3.10 B-splines added to the base-cylinder . . . . . . . . . . . . . . 38 3.11 Flowchart of how the evaluation of different b-spline- and

    random X and Y coordinate solutions are set up . . . . . . . 41

    4.1 Fitness-landscape of amplitude-search . . . . . . . . . . . . . 44 4.2 Best performing amplitude search . . . . . . . . . . . . . . . 45 4.3 Fitness landschape for different amplitudes and cycles . . . 47 4.4 Best performing amplitude and range search . . . . . . . . . 48 4.5 Fitness-landscape of absolute sine and range search . . . . . 50 4.6 Best performing absolute sinusoid . . . . . . . . . . . . . . . 51

    5.1 Evolution for single b-spline over entire mesh . . . . . . . . . 55 5.2 Best performing specimen for b-spline over entire mesh . . . 56 5.3 Evolution for single b-spline over the middle mesh . . . . . 58 5.4 Three best uniform meshes . . . . . . . . . . . . . . . . . . . 59

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  • 5.5 Evolution for two b-splines over the middle of the mesh . . 62 5.6 Three best half b-spline meshes . . . . . . . . . . . . . . . . . 63 5.7 Evolution for four b-splines over the middle of the mesh . . 66 5.8 Three best quarter b-spline meshes . . . . . . . . . . . . . . . 67 5.9 Evolution for random X and Y coordinates . . . . . . . . . . 69 5.10 Best perfroming random X and Y optimized mesh . . . . . . 70

    6.1 Stretching of sine-based actuators. . . . . . . . . . . . . . . .