{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Implements motion planning algorithms including path planners for N-D grids, trajectory optimizers, path-tracking controllers, and visualization tools for robotics applications.","skillfed_tags":["robotics","path-planning","trajectory-optimization"],"use_cases":["Prototyping path-planning solutions for mobile robots in obstacle-filled environments","Testing trajectory optimization and smoothing techniques for motion control","Learning and teaching motion planning algorithms with working reference implementations","Simulating multi-robot coordination using the toy 2D simulator with multiple robot types","Benchmarking different planner algorithms on 2D and 3D grid problems"],"what_it_does":"Python Motion Planning provides a collection of motion planning implementations for robotics, organized into three main components: path planners that find collision-free routes on N-D grids using graph search (Dijkstra, A*, JPS, Theta*) and sampling-based methods (RRT, RRT*); trajectory optimizers that generate smooth curves respecting kinematic and dynamic constraints; and path-tracking controllers (Pure Pursuit, PID, DWA, APF) that guide robots along planned paths. It includes a matplotlib-based visualizer and a toy 2D physics simulator for testing controllers on circular and differential-drive robots.\n\nThe package targets roboticists and autonomous systems developers who need reference implementations of standard planning algorithms. It depends on numpy, scipy, and numba for numerical computation, matplotlib and pyvista for visualization, and gymnasium for environment interaction. The repository is actively maintained with recent updates, though some algorithms from earlier versions (LQR, MPC, evolutionary methods) have not been migrated to the current release.","worth_installing":"Yes, with conditions. Install if you need reference implementations of standard motion planning algorithms for research, education, or prototyping. The low install friction, active maintenance, and lack of known vulnerabilities support adoption. However, be aware of the GPLv3 copyleft requirement if you plan to integrate this into proprietary code, and verify that the specific algorithms you need are present in version 2.0.1 (some have not been migrated from earlier releases)."},"id":"python-motion-planning","links":{"html":"https://skillfed.io/packages/python-motion-planning","md":"https://skillfed.io/packages/python-motion-planning.md","pypi":"https://pypi.org/project/python-motion-planning/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-02","license_spdx":null,"license_treatment":"copyleft","name":"python-motion-planning","python_support":"supports_current","summary":"Motion planning algorithms for Python"},"popularity":{"monthly_downloads":127226,"position":11750,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.0.1"}