Sorotoki - a MATLAB toolkit for soft robotics

Sorotoki is an open-source MATLAB toolkit for soft robotics that aims to facilitate the development of novel research in the field by providing a comprehensive set of tools for design, modeling, and control. The toolkit includes a diverse array of scientific disciplines relevant to soft robotics, such as continuum mechanics, dynamic systems and control theory, topology optimization, and computer graphics. The Sorotoki toolkit aims to make it easier for new researchers to learn about soft robotics by providing a range of tools that cover various important aspects of the field. This can significantly reduce the amount of time and effort required to get up to speed on the topic.

What can Sorotoki do?

Sorotoki has grown significantly since its inception and now offers a wide range of functionalities tailored to various aspects of soft robotics. For example, suppose you are inspired by the multi-gait soft crawler developed by Shepard et al.¹ -- See video. Or you want to simulate the incredibly dexterous soft gripper developed by Suzumori et al.² -- See video. Can we model such systems easily? Well.... Short answer: no. As matter of fact, simplifying the (infinite-dimensional) dynamics of soft robots is no easy feat. It has been an active topic of research for the better part of the last two decades. Ironically, studying "soft" robotics is incredibly "hard".

Sorotoki, however, can simplify the process of design, modeling and control for soft robots. Our aim is to make research into soft robotics more accessable by dividing the problem into smaller, more manageable steps. Multi-physics solvers are included into Sorotoki and can accessed with minimal programming complexity.

List of functionalities

To summarize, some of the functionalities included in the toolkit are listed below:

• Design: Implicit modeling using signed distance functions (SDFs), 2D-3D mesh generation, computational design using gradient-based optimization
• Modeling: Finite Element Models (FEM), high-efficiency reduced-order soft beam models (Lagrangian or port-Hamiltonian), easy programmable interconnections of a network of dynamical (soft robot) systems
• Control: Real-time (pneumatic) control platform using Raspberry Pi, vision-based sensing algorithms using Intel Realsense Depth camera
• Visualization: Fast and responsive 3D graphics rendering, mesh deformation modifiers (scaling, rotation, bending, twisting, mirroring), Forward Kinematic/Inverse Kinematic-rigging
• Accessibility: Minimal programming environment (i.e, focused on expressing complex problems with minimal lines of code).
• Open hardware: Four 3D-printable soft robots (e.g., soft hand, soft manipulator), and control interface).

Short examples

% generate mesh from image
msh = preset.mesh.pneunet;

% finite element solver (FEM)
fem = Fem(msh,'TimeStep', 1e-2);

% boundary conditions
fem = fem.addMaterial(NeoHookean(1, 0.4));
fem = fem.addGravity();
fem = fem.addPressure('allhole', 30 * 1e-3);
fem = fem.addSupport('left', [1,1]);

% magic
fem = fem.solve();

% take away message: simplicity > complexity

fem.replay('fps',60,...
           'axis',[-33, 120 -86 21],...
           'gif',true)

% CODE: Example 2 -- Material fitting using fmincon
[lam, str] = materialDataBase('DragonSkin10');

% define objective (Yeoh model)
obj = @(Pi) Objective(Pi,lam,str);

Pi0 = [0.0, 0.0, 0.0];  % initial guess
lb  = [1e-6, -2,  -2];  % lower bounds
ub  = [Inf, Inf, Inf];  % upper bounds

opt = optimoptions(@fmincon,'FiniteDifferenceStepSize',1e-12,  
                            'Algorithm','sqp-legacy');

fig(101,[9,9]);
Pi = fmincon(Obj,Pi0,[],[],[],[],lb,ub,[],opt);

function obj = Objective(Pi,X,Y)
    Q   = diag(Y.^-2);   % weighting - emphasis low strains  
    E   = Y - YeohMaterial(Pi).uniaxial(X); % stress resid.

    % quadratic lost function
    obj = E(:).' * Q * E(:);

    cla;
    plot(x, y, 'Color', color_light_gray); hold on;
    plot(x, YeohMaterial(Pi).uniaxial(x), ...
            'Color', color_visited_alt);
    ylabel('engineering stress $\sigma_{11}$');    
    xlabel('stretch $\lambda$');
end

% CODE: Example 2 -- Task-space controller
% assign desired setpoint
Xd  = [30; 10; 10]; 

% build continuum shape
POD = chebyspace(60, 3);            % POD basis
shp = Shapes(POD,[0,3,3,0,0,0], ... % pure bending XY
                'Material', NeoHookean(0.01,0.3));

% geometry and boundary conditions
shp = shp.setRadius(5);   % R = 5 mm
shp = shp.setRamp(0.75);  % R is reduced by 75% at s=L
shp = shp.addGravity();

% assign controller
shp.system.Controller = @(x) Control(x,Xd);

% magic ;)
shp = shp.simulate();

% task-space controller (called by solver)
function tau = tau(mdl,Xd)
  J = shp.system.Jacobian(4:6,:,end);
  ve = shp.system.Velocity(4:6,:,end);
  fe = shp.system.fElastic;
  fg = shp.system.fBody;
  x  = shp.system.Backbone(1:3,4,end);

  [kd, kp] = deal(5e-6);

  tau = fe + fg + kp * J.'*(Xd(:) - x) - kd * J.' * ve;
end

% Example 2b: plotting Model data
% for loop over Model log files
for ii = 1:numel(mdl.Log.t)  

    shp = shp.render(mdl.Log.x(ii,1:6)); % render shape

    if ii == 1, % render setpoint Xd
      plotpoint(Xd);  
    end

    axis([0 100 -5, 5, -10, 30]);
    view(30,30);
    drawnow;
end

% CODE: Example 3 -- Open-loop controller
% connect to controller board
brd = Bdog('pi','192.168.0.2','pwd', ...
           'NVeab',3);

% set board update frequency
brd = brd.set('Frequency',120);

% phase offset per finger
phi = @(k) (k-1)*pi/6;

%% execute control loop for T=10s
while brd.loop(10)
    T = ones(5,1) * brd.t;

    Pd = zeros(1,6);
    Pd(1:5) = 80 * sign(sin(4 * T - phi(1:5).')) * ...
              smoothstep(t-1);

    brd.setInput(Pd);
end

% disconnect system
brd.disconnect();

How to cite?

If you are planning on using Sorotoki in your (academic) work, please consider citing the toolkit

@misc{Caasenbrood2020,
  author = {Caasenbrood, Brandon},
  title = {Sorotoki - A Soft Robotics Toolkit for MATLAB},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/BJCaasenbrood/SorotokiCode}},
}