copp-py 0.2.2

Convex-objective path parameterization for robotic trajectory planning.

COPP Python Bindings

Convex-Objective Path Parameterization

This directory contains the Python package for COPP. Install the open-source distribution as copp-py and import the Python package with import copp_py as copp. It wraps the Rust solver core through PyO3 while presenting a NumPy-friendly interface for paths, robot constraints, solver options, and post-processing helpers.

COPP solves optimal path-parameterization problems. A geometric path

$$ q = q(s) $$

is converted into a time law

$$ s = s(t) $$

so the executed trajectory q(s(t)) satisfies velocity, acceleration, jerk, torque, or user-supplied constraints. The second-order solvers optimize the profile

$$ a(s) = \dot{s}^2 $$

and the third-order solvers optimize the pair

$$ a(s) = \dot{s}^2,\qquad b(s) = \ddot{s}. $$

The Python API follows the Rust crate layout: core modeling namespaces live under copp_py.path, copp_py.robot, copp_py.constraints, copp_py.objective, copp_py.interpolation, and copp_py.clarabel, while algorithms live under copp_py.solver.<algorithm>. Examples import copp_py as copp, so user code can use the short copp.Path and copp.solver.* aliases. This README focuses on installing, building, running examples, and using the Python interface. For the full project overview, benchmark tables, citation information, and collaboration contact details, see the COPP GitHub README.

Open-source / PRO note: this README documents the open-source Python package distributed as copp-py and imported as copp_py. COPP PRO provides additional licensed solvers and support options; see the repository-level PRO section or contact [e-mail] if those capabilities are relevant to your application.

The Python bindings follow a deliberately small set of rules:

• install the distribution as copp-py and import the Python module with import copp_py as copp;
• pass numerical data as NumPy-compatible arrays or ordinary Python sequences;
• use float64 data for predictable behavior and fewer copies;
• build paths with copp.Path, constraints with copp.Robot, and solver inputs with solver-specific Problem classes;
• call algorithms through Rust-like solver modules such as copp.solver.topp2_ra, copp.solver.copp2_socp, and copp.solver.copp3_socp;
• use copp.interpolation for profile-to-time conversion helpers.

API Availability

Problem class	Python API
Core utilities	copp.core, root aliases for version, __version__, errors, and common enums
Path	copp.path.Path, spline paths, evaluator paths, path derivative evaluation
Robot	copp.robot.Robot, station grids, sampled path derivatives, velocity/acceleration/jerk limits, raw constraints, inverse-dynamics callbacks
TOPP2	copp.solver.topp2_ra.solve, copp.solver.reach_set2.backward, copp.solver.reach_set2.bidirectional
COPP2	copp.solver.copp2_socp.solve, copp.solver.copp2_socp.solve_expert
TOPP3	copp.solver.topp3_lp.solve, copp.solver.topp3_lp.solve_expert, copp.solver.topp3_socp.solve, copp.solver.topp3_socp.solve_expert
COPP3	copp.solver.copp3_socp.solve, copp.solver.copp3_socp.solve_expert
Objectives	copp.objective.Time, ThermalEnergy, TotalVariationTorque, Linear
Interpolation	copp.interpolation.s_to_t_topp2, t_to_s_topp2_uniform, s_to_t_topp3, t_to_s_topp3_uniform, sample-based variants

Runnable examples are in examples. The Sphinx tutorials include those same files with literalinclude, so examples and documentation stay aligned.

Quick Start

Prerequisites

For the published Python package, you need:

• Python 3.9 or newer;
• numpy;
• jax for examples that build differentiable paths with Path.from_jax.

Create and activate any Python environment you prefer before running the commands below. The commands intentionally avoid machine-specific activation scripts, user directories, or environment names.

Install from PyPI

Install COPP with pip:

python -m pip install -U pip
python -m pip install -U copp-py

For examples that use Path.from_jax, install JAX as well:

python -m pip install -U jax

After installation:

python -c "import copp_py as copp; print(copp.version())"

Build and Install from Source

Use the source build path when you are working from a checkout or changing the Rust/Python binding code. You also need Rust, Cargo, a native compiler toolchain suitable for Rust extension modules, and maturin.

Install the local build tools once:

python -m pip install -U maturin numpy jax

Run from the repository root:

cargo build --release --lib --features python
maturin develop --release --features python

The Cargo command makes the Rust/Python feature build explicit. maturin develop then builds and installs the Python extension module into the active Python environment. After the build:

python -c "import copp_py as copp; print(copp.version())"

Run Examples

Run examples from the repository root after installing the package:

python bindings/python/examples/topp2_ra.py
python bindings/python/examples/copp2_socp.py
python bindings/python/examples/topp3_socp.py
python bindings/python/examples/copp3_socp.py
python bindings/python/examples/reach_set2.py

General Workflow

Most Python scripts follow the same shape:

1. Build a path from waypoints or a Python evaluator object.
2. Build a station grid s.
3. Create a copp.Robot.
4. Append stations and sample path derivatives into the robot.
5. Add velocity, acceleration, jerk, torque, or raw constraints.
6. Build a Problem descriptor for the chosen solver family.
7. Call a solver.
8. Convert the returned path-domain profile into t(s) or s(t) samples.
9. Evaluate the original path at s(t) for downstream control or plotting.

For second-order problems, the solver output is usually an a profile sampled on the station grid. For third-order problems, the output is a Profile3rd object with a and b profiles.

Path evaluation helpers return a consistent PathDerivatives object. For position-only evaluation, use the .q field:

out = path.evaluate_q(s)
q = out.q

Higher-order calls fill more fields on the same result type:

out = path.evaluate_up_to_2nd(s)
q = out.q
dq = out.dq
ddq = out.ddq

Minimal Program

import copp_py as copp

print("COPP version:", copp.version())

TOPP2-RA Example

This complete example builds a three-axis path with JAX, lets Path.from_jax provide the path derivatives, adds symmetric velocity and acceleration limits, solves TOPP2-RA, and converts the result into uniform time samples.

import numpy as np
import copp_py as copp


def main() -> None:
    try:
        import jax
        import jax.numpy as jnp
    except ImportError as exc:
        raise SystemExit("Install JAX to run this example: python -m pip install jax") from exc

    jax.config.update("jax_enable_x64", True)

    dim = 3
    n = 1001
    dt = 1.0e-3

    # 1) Define q(s). Path.from_jax differentiates it up to third order.
    def q_fn(s):
        freq = jnp.array([2.0 * jnp.pi, 3.0 * jnp.pi, 5.0 * jnp.pi], dtype=jnp.float64)
        phase = jnp.array([0.0, 0.3, 0.7], dtype=jnp.float64)
        return jnp.sin(freq * s + phase)

    path = copp.Path.from_jax(q_fn, 0.0, 1.0)
    s = np.linspace(0.0, 1.0, n, dtype=np.float64)

    # 2) Build robot constraints, then apply symmetric velocity and acceleration limits in [-1, 1].
    robot = copp.Robot(dim, capacity=n)
    robot.append_s(s)
    robot.set_q_from_path_2nd(path, 0, n)

    upper = np.ones(dim, dtype=np.float64)
    lower = -upper
    robot.add_velocity_limits(upper, lower, start_idx_s=0, length=n)
    robot.add_acceleration_limits(upper, lower, start_idx_s=0, length=n)

    # 3) Solve TOPP2-RA with boundary values a(0) = 0 and a(1) = 0.
    problem = copp.solver.topp2_ra.Problem(
        robot.constraints,
        idx_s_interval=(0, n - 1),
        a_boundary=(0.0, 0.0),
    )
    options = copp.solver.topp2_ra.Options()
    a_profile = copp.solver.topp2_ra.solve(problem, options)

    # 4) Post-process TOPP2-RA results: a(s) -> t(s) -> s(t).
    t_final, t_s = copp.interpolation.s_to_t_topp2(s, a_profile, 0.0)
    s_t = copp.interpolation.t_to_s_topp2_uniform(
        s,
        a_profile,
        t_s,
        dt,
        t0=0.0,
        include_final=True,
    )

    # 5) Print the tutorial summary.
    print("TOPP2-RA done.")
    print(f"dim = {dim}, N = {n}")
    print(f"t_final = {t_final:.6f} s")
    print(f"a_profile.len() = {len(a_profile)}")
    print(f"s(t) samples = {len(s_t)}")


if __name__ == "__main__":
    main()

The same structure extends to COPP2 by replacing the TOPP2 problem with copp.solver.copp2_socp.Problem and an objective list, and to third-order solvers by using set_q_from_path_3rd, jerk constraints, copp.solver.topp3_socp.Problem or copp.solver.copp3_socp.Problem, and the TOPP3 interpolation helpers.

Solver Namespaces

copp.solver.topp2_ra and copp.solver.reach_set2

TOPP2 is the second-order time-optimal family. It optimizes a(s) under first- and second-order constraints. Use copp.solver.topp2_ra.solve for the reachability-analysis solver and copp.solver.reach_set2.backward / bidirectional when you need reachable-set bounds directly.

copp.solver.copp2_socp

COPP2 solves second-order convex-objective problems. Objectives are constructed through copp.objective, for example:

objectives = [
    copp.objective.Time(1.0),
    copp.objective.ThermalEnergy(0.1, np.ones(dim, dtype=np.float64)),
]

Use copp.solver.copp2_socp.solve for the Clarabel SOCP formulation. Use copp.solver.copp2_socp.solve_expert when application code needs solver status and diagnostics instead of only the accepted profile.

copp.solver.topp3_lp and copp.solver.topp3_socp

TOPP3 is the third-order time-optimal family. It uses the (a,b) state and supports jerk-aware constraints. Use copp.solver.topp3_lp.solve for the linear-objective approximation or copp.solver.topp3_socp.solve for the Clarabel conic formulation. A common pattern is to generate an initial a profile with TOPP2-RA, substitute it into the constraints, then solve the third-order problem with LP or SOCP.

copp.solver.copp3_socp

COPP3 combines third-order constraints with convex objectives. Use copp.solver.copp3_socp.solve for the Clarabel SOCP formulation. Third-order solvers return Profile3rd objects that can be post-processed with copp.interpolation.s_to_t_topp3 and copp.interpolation.t_to_s_topp3_uniform.

Data Conventions

Python inputs are accepted as NumPy-compatible array-like values. At the wrapper boundary, arrays are validated and converted into contiguous float64 buffers when needed. To reduce copies in hot loops, pass numpy.ndarray values with dtype=np.float64 and C-contiguous layout unless the function documents another layout.

Path-sampled matrices commonly use sample-major layout, where each row is one station and each column is one axis. The MatrixLayout enum and path helpers document the accepted alternatives.

Path.evaluate_q, Path.evaluate_up_to_2nd, and Path.evaluate_up_to_3rd all return PathDerivatives. evaluate_q fills only out.q; derivative fields are None. This keeps path evaluation calls structurally consistent while making the requested derivative order explicit in the method name.

Boundary values are expressed in path-domain variables:

• a_boundary=(a_start, a_final) fixes a = ds/dt * ds/dt;
• b_boundary=(b_start, b_final) fixes b = d2s/dt2 for third-order problems.

Error Handling

Python argument-format errors are reported as standard Python exceptions such as TypeError or ValueError. Errors returned by the Rust core are exposed as copp.CoppError and typed subclasses such as PathError and ConstraintError.

try:
    a_profile = copp.solver.topp2_ra.solve(problem, options)
except copp.CoppError as exc:
    print("COPP failed:", exc)

For Clarabel-based solvers, the simple solver functions return an accepted profile or raise an exception. Expert variants such as copp.solver.copp2_socp.solve_expert and copp.solver.topp3_socp.solve_expert expose solver status, residuals, and other diagnostic fields for applications that need status-aware behavior.

Documentation

The Python documentation is generated with Sphinx from:

bindings/python/docs/source/

Install documentation dependencies:

python -m pip install -U sphinx

Build the HTML documentation from the repository root:

python -m sphinx -E -b html bindings/python/docs/source bindings/python/docs/build/html

Open the generated entry page after the build:

bindings/python/docs/build/html/index.html

The documentation is organized as:

• Guide: quick start, mathematical concepts, solver selection, tutorials, and how-to pages;
• Reference: API pages generated from Python modules and PyO3 docstrings.

Guide pages use the same path-parameterization variables as the Rust docs, and tutorial pages include runnable files from bindings/python/examples.

Package Layout

bindings/python/
  README.md
  copp_py/
    __init__.py          # public package facade
    core.py              # shared enums, version, and errors
    path.py              # path constructors and evaluation
    robot.py             # robot sampling and high-level constraints
    constraints.py       # raw constraint buffer namespace
    objective.py         # objective constructors
    interpolation.py     # profile/time conversion helpers
    clarabel.py          # Clarabel options and diagnostics
    solver/              # solver namespaces
      topp2_ra.py
      reach_set2.py
      copp2_socp.py
      topp3_lp.py
      topp3_socp.py
      copp3_socp.py
  docs/
    source/              # Sphinx source
    build/html/          # generated HTML output
  examples/              # runnable Python examples

The native extension module is built as copp_py._native.

Troubleshooting

import copp_py as copp Fails

Install the published package into the active Python environment:

python -m pip install -U copp-py

If you are working from a source checkout, build and install the local extension instead:

cargo build --release --lib --features python
maturin develop --release --features python

Then verify that the same interpreter can import the package:

python -c "import sys, copp_py as copp; print(sys.executable); print(copp.version())"

Sphinx Cannot Import copp_py

Install the package first with python -m pip install -U copp-py, or build the local checkout with cargo build --release --lib --features python followed by maturin develop --release --features python. Then run the Sphinx command using the same Python interpreter.

Native Build Fails

For source builds, check that Rust, Cargo, Python headers, and the platform compiler toolchain are available. On Windows, install a Visual Studio C++ build toolchain compatible with your Python interpreter. On Linux and macOS, ensure that the usual compiler and linker tools are available on PATH.

Array Shape or Type Errors

Convert inputs explicitly before calling into COPP:

values = np.ascontiguousarray(values, dtype=np.float64)

For path samples, verify the intended matrix layout and station count. Most robot-building helpers expect lengths to match the station grid already stored in Robot.