pyDynaMapp.trajectory package

Submodules

pyDynaMapp.trajectory.fourier module

class pyDynaMapp.trajectory.fourier.FourierGenerator(trajectory_params: dict)

Bases: object

Base class for peroidic trajectories generation.

Ref:

Fourier-based optimal excitation trajectories for the dynamic identification of robots Kyung.Jo Park - Robotica - 2006.

computeConstraintedDifferentiationError(ti: float, tf: float, x, q0=None, qp0=None, qpp0=None)

computeDifferentiationError(ti: float, tf: float, x, q0=None, qp0=None, qpp0=None)

computeFullTrajectory(ti: float, tf: float, q0=None, qp0=None, qpp0=None)

Computes the full trajectory data between ti and tf

computeProba(eps_n, J_n, bound)

computeTrajectoryCriterion(ti: float, tf: float, x, q0=None, qp0=None, qpp0=None) → float

Compute the trajectory identification criteria. The criteria mesures how the target x parmters changes du to a variation in W or in mesured torques for a linear or /linearized system τ ≈ W(q,qp,qpp,x0)x we use the Gramian matrix in calculations WTW wich is sysmetric square the cond number expressed as lamdamax/lamdmin.

computeTrajectoryError(x, tspan, new_traj_params=None, q0=None, qp0=None, qpp0=None, verbose=False)

computeTrajectoryIdentifiability(ti, tf, torque, q, qp, qpp, x)

evaluates the rgression criteria ε(qi,qpi,qppi,x) , en fonction du trajectory certain computed pervoiously C(qi,qpi,qppi) for fixed x system paramter vector : > the traj is identificable if the crietria is minimal in the most of time steps TODO : find the rlation betwen the rgression crirtia evolution and the trajectory C over time t

computeTrajectoryState(t: float = 0, q0=None, qp0=None, qpp0=None)

Computes the trajectory states at time date t

save2csv(ti: float, tf: float, file_path, q0=None, qp0=None, qpp0=None)

compute a given trajectory and save it to csv file.

visualizeTrajectory(ti, tf, q0=None, qp0=None, qpp0=None, savePath=None)

Compute and plot a given trajectory, each variable in a single plot

visualizeTrajectoryIdentifiability(ti, tf, torque, q, qp, qpp, x)

Visualize the trajectory identifiability criteria

modeled by the probobilty function: f(x) = P( abs((ε - εm)/σε) < 0.05 || abs((J - Jm)/σJ) < x) where x > 0

pyDynaMapp.trajectory.spline module

class pyDynaMapp.trajectory.spline.SplineGenerator(trajectory_params)

Bases: object

computeFullTrajectory(ti: float, tf: float, q0=None, qp0=None, qpp0=None)

Computes the full trajectory between ti and tf.

computeTrajectoryConstraints(qmax, qmin, qpmax, qpmin, qppmin, qppmax, ti, tf, q0=None, qp0=None, qpp0=None)

Ensures trajectory meets specified constraints.

computeTrajectoryIdentifiability()

Evaluates the regression criteria ε(q, qp, qpp, x).

computeTrajectoryState(t, Q0=None) → numpy.ndarray

Computes the trajectory states at time t.

saveTrajectory2file(filename='trajectory.txt')

Saves the computed trajectory to a file.

visualizeTrajectory(ti, tf, Q0=None, Qp0=None, Qpp0=None)

Visualizes the computed trajectory.

pyDynaMapp.trajectory.trajectory module

class pyDynaMapp.trajectory.trajectory.TrajectoryGenerator(ndof=7, sampling=1000, ti=0, tf=1000)

Bases: object

Base class for general tarjectory motion generation. it uqses polynomail Args:

• ndof - robot degree of freedom

• sampling - sampling time-genration frequancy

nbWaypoints - number of genated pointed of the trakejctory

plotTrajectory() → None

setTrajectoryData(time, Q, Qp, Qpp) → None

pyDynaMapp.trajectory.trapezoidal module

class pyDynaMapp.trajectory.trapezoidal.TrapezoidalGenerator(njoints, nwaypoints, acc, delta_t1, delta_t2, Nf)

Bases: object

Base class for trapezoidal trajectories generation Args:

• njoints number of joints

• nwaypoints number of waypoints

  acceleration values accelerated durations vel constant durations runtime

Output:

q, qd, qdd

TrapezoidalGenerator()

initConfig()

trapTraj_PTP(a1, q0, n1, n2, N)

visualizeTrajectory()

Module contents