Add a planar pcs system with pneumatic actuation

examples/simulate_planar_pcs.py

@@ -162,7 +162,7 @@ def draw_robot(
         )
         plt.plot(
             video_ts, q_ts[:, 3 * segment_idx + 2],
-            label=r"$\sigma_\mathrm{el," + str(segment_idx + 1) + "}$ [-]"
+            label=r"$\sigma_\mathrm{ax," + str(segment_idx + 1) + "}$ [-]"
         )
     plt.xlabel("Time [s]")
     plt.ylabel("Configuration")

examples/simulate_pneumatic_planar_pcs.py

@@ -0,0 +1,273 @@
+from functools import partial
+import jax
+
+jax.config.update("jax_enable_x64", True)  # double precision
+from diffrax import diffeqsolve, Euler, ODETerm, SaveAt, Tsit5
+from jax import Array, vmap
+from jax import numpy as jnp
+import matplotlib.pyplot as plt
+import numpy as onp
+from pathlib import Path
+from typing import Callable, Dict
+
+import jsrm
+from jsrm import ode_factory
+from jsrm.systems import pneumatic_planar_pcs
+
+num_segments = 1
+
+# filepath to symbolic expressions
+sym_exp_filepath = (
+    Path(jsrm.__file__).parent
+    / "symbolic_expressions"
+    / f"planar_pcs_ns-{num_segments}.dill"
+)
+
+# set parameters
+rho = 1070 * jnp.ones((num_segments,))  # Volumetric density of Dragon Skin 20 [kg/m^3]
+params = {
+    "th0": jnp.array(0.0),  # initial orientation angle [rad]
+    "l": 1e-1 * jnp.ones((num_segments,)),
+    "r": 2e-2 * jnp.ones((num_segments,)),
+    "rho": rho,
+    "g": jnp.array([0.0, 9.81]),
+    "E": 2e3 * jnp.ones((num_segments,)),  # Elastic modulus [Pa]
+    "G": 1e3 * jnp.ones((num_segments,)),  # Shear modulus [Pa]
+    "r_cham_in": 5e-3 * jnp.ones((num_segments,)),
+    "r_cham_out": 2e-2 - 2e-3 * jnp.ones((num_segments,)),
+    "varphi_cham": jnp.pi/2 * jnp.ones((num_segments,)),
+}
+params["D"] = 5e-4 * jnp.diag(
+    (jnp.repeat(
+        jnp.array([[1e0, 1e3, 1e3]]), num_segments, axis=0
+    ) * params["l"][:, None]).flatten()
+)
+
+# activate all strains (i.e. bending, shear, and axial)
+# strain_selector = jnp.ones((3 * num_segments,), dtype=bool)
+strain_selector = jnp.array([True, False, True])[None, :].repeat(num_segments, axis=0).flatten()
+
+B_xi, forward_kinematics_fn, dynamical_matrices_fn, auxiliary_fns = (
+    pneumatic_planar_pcs.factory(num_segments, sym_exp_filepath, strain_selector)
+)
+# jit the functions
+dynamical_matrices_fn = jax.jit(dynamical_matrices_fn)
+actuation_mapping_fn = partial(
+    auxiliary_fns["actuation_mapping_fn"],
+    forward_kinematics_fn,
+    auxiliary_fns["jacobian_fn"],
+)
+
+def sweep_local_tip_force_to_bending_torque_mapping():
+    def compute_bending_torque(q: Array) -> Array:
+        # backbone coordinate of the end-effector
+        s_ee = jnp.sum(params["l"])
+        # compute the pose of the end-effector
+        chi_ee = forward_kinematics_fn(params, q, s_ee)
+        # orientation of the end-effector
+        th_ee = chi_ee[2]
+        # compute the jacobian of the end-effector
+        J_ee = auxiliary_fns["jacobian_fn"](params, q, s_ee)
+        # local tip force
+        f_ee_local = jnp.array([0.0, 1.0])
+        # tip force in inertial frame
+        f_ee = jnp.array([[jnp.cos(th_ee), -jnp.sin(th_ee)], [jnp.sin(th_ee), jnp.cos(th_ee)]]) @ f_ee_local
+        # compute the generalized torque
+        tau_be = J_ee[:2, 0].T @ f_ee
+        return tau_be
+
+    kappa_be_pts = jnp.arange(-2*jnp.pi, 2*jnp.pi, 0.01)
+    sigma_ax_pts = jnp.zeros_like(kappa_be_pts)
+    q_pts = jnp.stack([kappa_be_pts, sigma_ax_pts], axis=-1)
+
+    tau_be_pts = vmap(compute_bending_torque)(q_pts)
+
+    # plot the mapping on the bending strain
+    fig, ax = plt.subplots(num="planar_pcs_local_tip_force_to_bending_torque_mapping")
+    plt.title(r"Mapping from $f_\mathrm{ee}$ to $\tau_\mathrm{be}$")
+    ax.plot(kappa_be_pts, tau_be_pts, linewidth=2.5)
+    ax.set_xlabel(r"$\kappa_\mathrm{be}$ [rad/m]")
+    ax.set_ylabel(r"$\tau_\mathrm{be}$ [N m]")
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
+
+def sweep_actuation_mapping():
+    # evaluate the actuation matrix for a straight backbone
+    q = jnp.zeros((2 * num_segments,))
+    A = actuation_mapping_fn(params, B_xi, q)
+    print("Evaluating actuation matrix for straight backbone: A =\n", A)
+
+    kappa_be_pts = jnp.linspace(-3*jnp.pi, 3*jnp.pi, 500)
+    sigma_ax_pts = jnp.zeros_like(kappa_be_pts)
+    q_pts = jnp.stack([kappa_be_pts, sigma_ax_pts], axis=-1)
+    A_pts = vmap(actuation_mapping_fn, in_axes=(None, None, 0))(params, B_xi, q_pts)
+    # mark the points that are not controllable as the u1 and u2 terms share the same sign
+    non_controllable_selector = A_pts[..., 0, 0] * A_pts[..., 0, 1] >= 0.0
+    non_controllable_indices = jnp.where(non_controllable_selector)[0]
+    non_controllable_boundary_indices = jnp.where(non_controllable_selector[:-1] != non_controllable_selector[1:])[0]
+    # plot the mapping on the bending strain for various bending strains
+    fig, ax = plt.subplots(num="pneumatic_planar_pcs_actuation_mapping_bending_torque_vs_bending_strain")
+    plt.title(r"Actuation mapping from $u$ to $\tau_\mathrm{be}$")
+    # # shade the region where the actuation mapping is negative as we are not able to bend the robot further
+    # ax.axhspan(A_pts[:, 0, 0:2].min(), 0.0, facecolor='red', alpha=0.2)
+    for idx in non_controllable_indices:
+        ax.axvspan(kappa_be_pts[idx], kappa_be_pts[idx+1], facecolor='red', alpha=0.2)
+    ax.plot(kappa_be_pts, A_pts[:, 0, 0], linewidth=2, label=r"$\frac{\partial \tau_\mathrm{be}}{\partial u_1}$")
+    ax.plot(kappa_be_pts, A_pts[:, 0, 1], linewidth=2, label=r"$\frac{\partial \tau_\mathrm{ax}}{\partial u_2}$")
+    ax.set_xlabel(r"$\kappa_\mathrm{be}$ [rad/m]")
+    ax.set_ylabel(r"$\frac{\partial \tau_\mathrm{be}}{\partial u_1}$")
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
+    # create grid for bending and axial strains
+    kappa_be_grid, sigma_ax_grid = jnp.meshgrid(
+        jnp.linspace(-jnp.pi, jnp.pi, 20),
+        jnp.linspace(-0.2, 0.2, 20),
+    )
+    q_pts = jnp.stack([kappa_be_grid.flatten(), sigma_ax_grid.flatten()], axis=-1)
+
+    # evaluate the actuation mapping on the grid
+    A_pts = vmap(actuation_mapping_fn, in_axes=(None, None, 0))(params, B_xi, q_pts)
+    # reshape A_pts to match the grid shape
+    A_grid = A_pts.reshape(kappa_be_grid.shape[:2] + A_pts.shape[-2:])
+
+    # plot the mapping on the bending strain
+    fig, ax = plt.subplots(num="pneumatic_planar_pcs_actuation_mapping_bending_torque_vs_axial_vs_bending_strain")
+    plt.title(r"Actuation mapping from $u_1$ to $\tau_\mathrm{be}$")
+    # contourf plot
+    c = ax.contourf(kappa_be_grid, sigma_ax_grid, A_grid[..., 0, 0], levels=100)
+    fig.colorbar(c, ax=ax, label=r"$\frac{\partial \tau_\mathrm{be}}{\partial u_1}$")
+    # contour plot
+    ax.contour(kappa_be_grid, sigma_ax_grid, A_grid[..., 0, 0], levels=20, colors="k", linewidths=0.5)
+    ax.set_xlabel(r"$\kappa_\mathrm{be}$ [rad/m]")
+    ax.set_ylabel(r"$\sigma_\mathrm{ax}$ [-]")
+    plt.tight_layout()
+    plt.show()
+
+    # plot the mapping on the axial strain
+    fig, ax = plt.subplots(num="pneumatic_planar_pcs_actuation_mapping_axial_torque_vs_axial_vs_bending_strain")
+    plt.title(r"Actuation mapping from $u_1$ to $\tau_\mathrm{ax}$")
+    # contourf plot
+    c = ax.contourf(kappa_be_grid, sigma_ax_grid, A_grid[..., 1, 0], levels=100)
+    fig.colorbar(c, ax=ax, label=r"$\frac{\partial \tau_\mathrm{ax}}{\partial u_1}$")
+    # contour plot
+    ax.contour(kappa_be_grid, sigma_ax_grid, A_grid[..., 1, 0], levels=20, colors="k", linewidths=0.5)
+    ax.set_xlabel(r"$\kappa_\mathrm{be}$ [rad/m]")
+    ax.set_ylabel(r"$\sigma_\mathrm{ax}$ [-]")
+    plt.tight_layout()
+    plt.show()
+
+
+def simulate_robot():
+    # define initial configuration
+    q0 = jnp.repeat(jnp.array([-5.0 * jnp.pi, -0.2])[None, :], num_segments, axis=0).flatten()
+    # number of generalized coordinates
+    n_q = q0.shape[0]
+
+    # set simulation parameters
+    dt = 1e-3  # time step
+    sim_dt = 5e-5  # simulation time step
+    ts = jnp.arange(0.0, 7.0, dt)  # time steps
+
+    x0 = jnp.concatenate([q0, jnp.zeros_like(q0)])  # initial condition
+    u = jnp.array([1.2e3, 0e0])  # control inputs (pressures in the right and left chambers)
+
+    ode_fn = ode_factory(dynamical_matrices_fn, params, u)
+    term = ODETerm(ode_fn)
+
+    sol = diffeqsolve(
+        term,
+        solver=Tsit5(),
+        t0=ts[0],
+        t1=ts[-1],
+        dt0=sim_dt,
+        y0=x0,
+        max_steps=None,
+        saveat=SaveAt(ts=ts),
+    )
+
+    print("sol.ys =\n", sol.ys)
+    # the evolution of the generalized coordinates
+    q_ts = sol.ys[:, :n_q]
+    # the evolution of the generalized velocities
+    q_d_ts = sol.ys[:, n_q:]
+
+    # evaluate the forward kinematics along the trajectory
+    chi_ee_ts = vmap(forward_kinematics_fn, in_axes=(None, 0, None))(
+        params, q_ts, jnp.array([jnp.sum(params["l"])])
+    )
+    # plot the configuration vs time
+    plt.figure()
+    for segment_idx in range(num_segments):
+        plt.plot(
+            ts, q_ts[:, 2 * segment_idx + 0],
+            label=r"$\kappa_\mathrm{be," + str(segment_idx + 1) + "}$ [rad/m]"
+        )
+        plt.plot(
+            ts, q_ts[:, 2 * segment_idx + 1],
+            label=r"$\sigma_\mathrm{ax," + str(segment_idx + 1) + "}$ [-]"
+        )
+    plt.xlabel("Time [s]")
+    plt.ylabel("Configuration")
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+    # plot end-effector position vs time
+    plt.figure()
+    plt.plot(ts, chi_ee_ts[:, 0], label="x")
+    plt.plot(ts, chi_ee_ts[:, 1], label="y")
+    plt.xlabel("Time [s]")
+    plt.ylabel("End-effector Position [m]")
+    plt.legend()
+    plt.grid(True)
+    plt.box(True)
+    plt.tight_layout()
+    plt.show()
+    # plot the end-effector position in the x-y plane as a scatter plot with the time as the color
+    plt.figure()
+    plt.scatter(chi_ee_ts[:, 0], chi_ee_ts[:, 1], c=ts, cmap="viridis")
+    plt.axis("equal")
+    plt.grid(True)
+    plt.xlabel("End-effector x [m]")
+    plt.ylabel("End-effector y [m]")
+    plt.colorbar(label="Time [s]")
+    plt.tight_layout()
+    plt.show()
+    # plt.figure()
+    # plt.plot(chi_ee_ts[:, 0], chi_ee_ts[:, 1])
+    # plt.axis("equal")
+    # plt.grid(True)
+    # plt.xlabel("End-effector x [m]")
+    # plt.ylabel("End-effector y [m]")
+    # plt.tight_layout()
+    # plt.show()
+
+    # plot the energy along the trajectory
+    kinetic_energy_fn_vmapped = vmap(
+        partial(auxiliary_fns["kinetic_energy_fn"], params)
+    )
+    potential_energy_fn_vmapped = vmap(
+        partial(auxiliary_fns["potential_energy_fn"], params)
+    )
+    U_ts = potential_energy_fn_vmapped(q_ts)
+    T_ts = kinetic_energy_fn_vmapped(q_ts, q_d_ts)
+    plt.figure()
+    plt.plot(ts, U_ts, label="Potential energy")
+    plt.plot(ts, T_ts, label="Kinetic energy")
+    plt.xlabel("Time [s]")
+    plt.ylabel("Energy [J]")
+    plt.legend()
+    plt.grid(True)
+    plt.box(True)
+    plt.tight_layout()
+    plt.show()
+
+if __name__ == "__main__":
+    sweep_local_tip_force_to_bending_torque_mapping()
+    sweep_actuation_mapping()
+    simulate_robot()

pyproject.toml

@@ -17,7 +17,7 @@ name = "jsrm"  # Required
 #
 # For a discussion on single-sourcing the version, see
 # https://packaging.python.org/guides/single-sourcing-package-version/
-version = "0.0.12"  # Required
+version = "0.0.13"  # Required
 
 # This is a one-line description or tagline of what your project does. This
 # corresponds to the "Summary" metadata field: