SCI — Sequential Contact Insertion
Continuous-time motion planning for robot fleets, using analytic cubic trajectories and a progressively constructed set of contacts.
SCI plans for at least two robots in two or more spatial dimensions with prescribed endpoint positions and velocities, a fixed horizon, and a shared minimum pairwise distance. It minimizes squared acceleration through local contact matching and checks separation over the entire trajectory.
Installation
Python 3.10 or newer is required. After the first PyPI release:
pip install sci-solver
To install directly from GitHub now:
python -m pip install git+https://github.com/jankammeth/sci.git
Or install a local checkout:
python -m pip install .
NumPy and SciPy are the only runtime dependencies. Supported binary wheels include
the C collision checker. A source installation builds it when a C compiler is
available; otherwise it uses the slower Python checker and emits a warning on use.
The chosen backend is reported in solution.metrics["collision_backend"].
Quick start
import numpy as np
from sci_solver import SolverConfig, solve
solution = solve(
initial_positions=[[-2, -0.2], [2, 0.2]],
final_positions=[[2, -0.2], [-2, 0.2]],
time_horizon=5.0,
min_distance=1.0,
options=SolverConfig(sci_detection_tol=1e-6),
)
if not solution.success:
raise RuntimeError(solution.metrics["convergence_reason"])
times = np.linspace(0, 5, 201)
positions = solution.sample(times) # (2 robots, 201 times, 2 dimensions)
velocities = solution.sample(times, derivative=1)
print(solution.acceleration_cost)
print(solution.metrics["min_distance"])
Omitted endpoint velocities are zero. Sampling is postprocessing and does not
determine whether a trajectory passes separation checks. solution.contacts
contains the solved contact states, and solution.trajectory exposes the cubic
coefficients and knot times.
Examples
python -m pip install '.[plot]'
python examples/two_robots.py --output outputs/two-robots.png
python examples/fleet.py --output outputs/fleet.png
sci-solve --smoke --output outputs/smoke.json
The plotting dependency is optional. Both examples also run without plotting when
--output is omitted. The CLI accepts saved NPZ endpoint states and a JSON config;
see the API guide.
Numerical scope
The solver uses the current contact insertion, contact removal, banded Newton, sparse fallback, and condensed reaction recovery implementation. It is a local numerical method: convergence and global optimality are not guaranteed. The continuous collision checks use floating-point polynomial calculations, rather than interval-arithmetic certificates. Obstacles and finite position, velocity, acceleration, or jerk bounds are outside this release's scope.
Always inspect solution.success. An unsuccessful solve retains its last iterate
and diagnostics; it is not a usable collision-free plan. Tolerances are absolute
and should be chosen for the physical scale of the problem. The reference default
separation allowance is zero; the examples explicitly use 1e-6.
Development and provenance
python -m pip install -e '.[dev,plot]'
python -m pytest
ruff check .
The numerical implementation is preserved from
contact-solver at 641a234.
UPSTREAM.json records source paths, SHA-256 hashes, and every
extraction edit. Six core files are byte-identical. The collision wrapper changes
only the package name in its fallback warning; the C kernel adds equivalent
stack workspace allocation for Microsoft's C compiler. Numerical formulas and
control flow are unchanged.
The migration guide describes the extraction and comparison
procedure. The paper and historical experiment infrastructure remain separate.
This software accompanies Contact-based motion planning for robot fleets. Software citation metadata is in CITATION.cff. Publication metadata for the manuscript will be added when available.
1 maintainer
Metadata
1 maintainer
Release files for sci-solver 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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|---|---|---|---|
| sci_solver-0.1.0.tar.gz | 60.6 kB | Details |
Built distributions (wheels)
Total release size: 1.1 MB
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