ToppraPlanner

ToppraPlanner#

ToppraPlanner is a trajectory planner based on the TOPPRA (Time-Optimal Path Parameterization via Reachability Analysis) library. It generates time-optimal joint trajectories under velocity and acceleration constraints.

Features#

  • Time-optimal trajectory generation: Computes the fastest possible trajectory between waypoints, given joint velocity and acceleration limits.

  • Flexible sampling: Supports sampling by time interval or by number of points.

  • Constraint handling: Automatically formats velocity and acceleration constraints for the TOPPRA solver.

  • Dense and sparse waypoints: Supports both dense and sparse waypoint interpolation.

Usage#

Initialization#

from embodichain.lab.sim.planners.toppra_planner import ToppraPlanner
planner = ToppraPlanner(
    dofs=6,
    max_constraints={
        "velocity": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
        "acceleration": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0]
    }
)

Planning#

from embodichain.lab.sim.planners.utils import TrajectorySampleMethod
from embodichain.lab.sim.planners.toppra_planner import ToppraPlanner
success, positions, velocities, accelerations, times, duration = planner.plan(
    current_state={
        "position": [0, 0, 0, 0, 0, 0],
        "velocity": [0, 0, 0, 0, 0, 0],
        "acceleration": [0, 0, 0, 0, 0, 0]
    },
    target_states=[
        {"position": [1, 1, 1, 1, 1, 1]}
    ],
    sample_method=TrajectorySampleMethod.TIME,
    sample_interval=0.01
)

  • positions, velocities, accelerations are arrays of sampled trajectory points.

  • times is the array of time stamps.

  • duration is the total trajectory time.

Notes#

  • The planner requires the toppra library (pip install toppra==0.6.3).

  • For dense waypoints, the default spline interpolation is used. For sparse waypoints, you may need to adjust the interpolation method.

  • The number of grid points (gridpt_min_nb_points) is important for accurate acceleration constraint handling.

References#

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