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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. Install from PyPI:

pip install sci-solver

To install directly from GitHub:

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.

Starting with 0.2.0, the default sci_fleet_mode="components" uses union-find to group robots connected by contacts and solves one system per group. Robots with no contacts follow their unconstrained trajectories. Unchanged groups reuse their solutions and checks. All modes check continuous separation across the entire fleet.

Select SolverConfig(sci_fleet_mode="full") to reproduce the earlier full-fleet formulation, or sci_fleet_mode="active" to solve one system containing all participating robots. The default uses the same component algorithm for every fleet size; it has no robot-count threshold. Small contacting fleets can incur extra bookkeeping overhead; the benchmarks report those cases as well. The component reuse benchmark compares the optimized driver with its prototype and full-fleet mode on saved 2-, 4-, and 20-robot cases, including a detailed Newton breakdown. Unchanged components retain their solved states and checks; graph rebuilding remains simple union-find.

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. Four core files remain 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 in the explicit full-fleet mode are retained; its driver records stage timings and dispatches the default component solver. Contact initialization, recovery, and validation are shared with the component driver; Newton has optional fine stage profiling. Original extraction hashes are retained alongside current hashes and change descriptions. 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.

Licensed under Apache-2.0; see LICENSE and NOTICE.

Metadata

Release files for sci-solver 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Source distribution for sci-solver 0.2.0
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sci_solver-0.2.0-cp314-cp314-win_amd64.whl CPython 3.14 CPython 3.14 Windows x86-64 Details
sci_solver-0.2.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.14 CPython 3.14 Linux glibc 2.5+ x86-64, Linux glibc 2.28+ x86-64 Details
sci_solver-0.2.0-cp314-cp314-macosx_11_0_arm64.whl CPython 3.14 CPython 3.14 macOS 11.0+ ARM64 Details
sci_solver-0.2.0-cp314-cp314-macosx_10_15_x86_64.whl CPython 3.14 CPython 3.14 macOS 10.15+ x86-64 Details
sci_solver-0.2.0-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
sci_solver-0.2.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.5+ x86-64, Linux glibc 2.28+ x86-64 Details
sci_solver-0.2.0-cp313-cp313-macosx_11_0_arm64.whl CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
sci_solver-0.2.0-cp313-cp313-macosx_10_13_x86_64.whl CPython 3.13 CPython 3.13 macOS 10.13+ x86-64 Details
sci_solver-0.2.0-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
sci_solver-0.2.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.5+ x86-64, Linux glibc 2.28+ x86-64 Details
sci_solver-0.2.0-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
sci_solver-0.2.0-cp312-cp312-macosx_10_13_x86_64.whl CPython 3.12 CPython 3.12 macOS 10.13+ x86-64 Details
sci_solver-0.2.0-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
sci_solver-0.2.0-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.28+ x86-64, Linux glibc 2.5+ x86-64 Details
sci_solver-0.2.0-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
sci_solver-0.2.0-cp311-cp311-macosx_10_9_x86_64.whl CPython 3.11 CPython 3.11 macOS 10.9+ x86-64 Details
sci_solver-0.2.0-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
sci_solver-0.2.0-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.5+ x86-64, Linux glibc 2.28+ x86-64 Details
sci_solver-0.2.0-cp310-cp310-macosx_11_0_arm64.whl CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details
sci_solver-0.2.0-cp310-cp310-macosx_10_9_x86_64.whl CPython 3.10 CPython 3.10 macOS 10.9+ x86-64 Details

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