dymoesco.dynamics.dynamic_models.DiffDrive

class dymoesco.dynamics.dynamic_models.DiffDrive(radius=1, u_std=0, y_std=0, max_radar_range=1, beacons=[], name=None)

Bases: dymoesco.dynamics.dynamic_models.ContinuousDynamicModel, dymoesco.dynamics.statespace_models.SE2

Differential drive wheeled robot, using velocity inputs (kinematics model).

DiffDrive is an implementation of a differential drive wheeled robot happening on the SE(2) state-space with variables \((x,y,\theta)\):

\[\begin{split}\dot{x} &= v \cos\theta \\ \dot{y} &= v \sin\theta \\ \dot{\theta} &= w / \text{radius}\end{split}\]

DiffDrive also has a range and bearing sensor, which has a max range of max_radar_range and the bearing sensor works irrespective of the position of the beacon (360deg coverage).

Parameters

  • radius (float) – radius of the wheels

  • u_std (float or list of 3 floats) – control noise standard deviation. Can also give different standard deviations for each state (x,y theta).

  • y_std (float or list of 2 floats) – sensor noise standard deviation. Can also give a different standard deviations for range and bearing.

  • max_radar_range (float) – maximum range for which the range sensor will return an observation

  • beacons (list of tuples) – list of beacon (x,y)-positions, one for each beacon. Beacons are sensed by the range-bearing sensor (_g/g methods)

__init__(radius=1, u_std=0, y_std=0, max_radar_range=1, beacons=[], name=None)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([radius, u_std, y_std, …])

Initialize self.

animate(traj[, ax])

discretize(dt)

f(x, u)

General dynamics function which is called in code.

g(z)

Observation function for beacons-based range/bearing sensor.

gui(key_to_u_map, x0[, ekf, show_pred, …])

plot(traj[, attr, ax])

traj needs to be a Trajectory object (or a dictionary with t and the requested attribute (x,y or u)).

plotu(traj[, ax])

plotx(traj[, ax])

ploty(traj[, ax])

simulate(u, t_span, x0[, t_eval])

simulate calls solve_ivp and returns a Trajectory object.
f(x, u)

General dynamics function which is called in code.

f adds some boiletplate around _f(), such as adding the noise to make the dynamical system stochastic: \(\dot{x} = f(x,u) = _f(x,u) + \epsilon\). In most cases only f will not need to be overridden.

g(z)

Observation function for beacons-based range/bearing sensor.

Note how we overrode dymoesco.dynamics.dynamic_models.ContinuousDynamicModel.g() to get the desired behavior.

plot(traj, attr='x', ax=None)

traj needs to be a Trajectory object (or a dictionary with t and the requested attribute (x,y or u)).

simulate(u, t_span, x0, t_eval=None)

simulate calls solve_ivp and returns a Trajectory object. u in the returned trajectory is the underlying noiseless trajectory. TODO: find a way to return the noisy trajectory? (get the u_noisy out of f).