# robotics-numpy
[](https://badge.fury.io/py/robotics-numpy)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://github.com/petercorke/robotics-toolbox-python)
A **lightweight, high-performance robotics library** inspired by Peter Corke's [robotics-toolbox-python](https://github.com/petercorke/robotics-toolbox-python), offering similar functionality with a strict **NumPy-only dependency**.
## Core Principles & Goals
* **Inspired by `robotics-toolbox-python`**: Mimics API, function usage, and overall approach for a familiar user experience.
* **NumPy-centric**: All core functionality relies solely on NumPy for minimal dependencies.
* **Simple**: Clean, intuitive API for easy learning.
Ideal for education, research, and applications where dependency management is critical.
## Quick Start
### Installation
```bash
# Core library (NumPy only)
pip install robotics-numpy
# With visualization support (requires Plotly)
pip install robotics-numpy[visualization]
```
### Basic Usage: Transformations
```python
import robotics_numpy as rn
import numpy as np
# Create transformations (mimicking robotics-toolbox API)
T1 = rn.SE3.Trans(1, 2, 3) # Translation
T2 = rn.SE3.RPY(0.1, 0.2, 0.3) # Rotation from RPY
T3 = T1 * T2 # Compose transformations
# Transform points
point = [0, 0, 0]
transformed_point = T3 * point
print(f"Transformed point: {transformed_point}")
```
### Basic Usage: Robot Kinematics (v0.2.0)
```python
import robotics_numpy as rn
import numpy as np
# Define a simple 2-DOF robot (similar to robotics-toolbox syntax)
robot = rn.models.DHRobot([
rn.models.RevoluteDH(d=0.1, a=0.2, alpha=0, qlim=[-np.pi, np.pi]),
rn.models.PrismaticDH(theta=0, a=0.1, alpha=0, qlim=[0, 0.5]),
])
# Forward kinematics
q = [np.pi / 4, 0.3]
pose = robot.fkine(q)
print(f"End-effector pose: {pose}")
# Jacobian
jacobian = robot.jacob0(q)
print(f"Jacobian:\n{jacobian}")
```
## Performance Comparison
This table benchmarks key operations, comparing `robotics-numpy` (NumPy-based) against `roboticstoolbox-python`.
```
================================================================================
Performance Comparison Table
================================================================================
| Feature | robotics-numpy | rtb | Difference (%) |
|---------------------|------------------------|------------------------|----------------|
| Forward Kinematics | 0.870 ms | 0.026 ms | +3189.61% |
| | 2.441 ms | 0.054 ms | +4432.86% |
| Jacobian | 1.997 ms | 0.074 ms | +2599.14% |
| | 5.520 ms | 0.158 ms | +3397.66% |
| Manipulability | 5.482 ms | 0.330 ms | +1559.99% |
================================================================================
```
## Contributing
We welcome contributions! Our focus is on:
* **Readability**: Clear algorithms over complex optimizations.
* **Testability**: Comprehensive unit and integration tests.
* **Documentation**: Well-documented code and examples.
```bash
# Development setup
git clone https://github.com/chaoyue/robotics-numpy
cd robotics-numpy
pip install -e .[dev] # Or use uv for dependency management
# Run tests
pytest
# Check code quality
ruff check .
mypy src/
```
## License
MIT License - see [LICENSE](LICENSE) for details.
## Acknowledgments
Inspired by Peter Corke's [Robotics Toolbox](https://github.com/petercorke/robotics-toolbox-python),
with a focus on minimal dependencies.
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"description": "# robotics-numpy\n\n[](https://badge.fury.io/py/robotics-numpy)\n[](https://opensource.org/licenses/MIT)\n[](https://www.python.org/downloads/)\n[](https://github.com/petercorke/robotics-toolbox-python)\n\nA **lightweight, high-performance robotics library** inspired by Peter Corke's [robotics-toolbox-python](https://github.com/petercorke/robotics-toolbox-python), offering similar functionality with a strict **NumPy-only dependency**.\n\n## Core Principles & Goals\n\n* **Inspired by `robotics-toolbox-python`**: Mimics API, function usage, and overall approach for a familiar user experience.\n* **NumPy-centric**: All core functionality relies solely on NumPy for minimal dependencies.\n* **Simple**: Clean, intuitive API for easy learning.\n\nIdeal for education, research, and applications where dependency management is critical.\n\n## Quick Start\n\n### Installation\n\n```bash\n# Core library (NumPy only)\npip install robotics-numpy\n\n# With visualization support (requires Plotly)\npip install robotics-numpy[visualization]\n```\n\n### Basic Usage: Transformations\n\n```python\nimport robotics_numpy as rn\nimport numpy as np\n\n# Create transformations (mimicking robotics-toolbox API)\nT1 = rn.SE3.Trans(1, 2, 3) # Translation\nT2 = rn.SE3.RPY(0.1, 0.2, 0.3) # Rotation from RPY\nT3 = T1 * T2 # Compose transformations\n\n# Transform points\npoint = [0, 0, 0]\ntransformed_point = T3 * point\nprint(f\"Transformed point: {transformed_point}\")\n```\n\n### Basic Usage: Robot Kinematics (v0.2.0)\n\n```python\nimport robotics_numpy as rn\nimport numpy as np\n\n# Define a simple 2-DOF robot (similar to robotics-toolbox syntax)\nrobot = rn.models.DHRobot([\n rn.models.RevoluteDH(d=0.1, a=0.2, alpha=0, qlim=[-np.pi, np.pi]),\n rn.models.PrismaticDH(theta=0, a=0.1, alpha=0, qlim=[0, 0.5]),\n])\n\n# Forward kinematics\nq = [np.pi / 4, 0.3]\npose = robot.fkine(q)\nprint(f\"End-effector pose: {pose}\")\n# Jacobian\njacobian = robot.jacob0(q)\nprint(f\"Jacobian:\\n{jacobian}\")\n```\n\n## Performance Comparison\n\nThis table benchmarks key operations, comparing `robotics-numpy` (NumPy-based) against `roboticstoolbox-python`.\n\n```\n================================================================================\nPerformance Comparison Table\n================================================================================\n| Feature | robotics-numpy | rtb | Difference (%) |\n|---------------------|------------------------|------------------------|----------------|\n| Forward Kinematics | 0.870 ms | 0.026 ms | +3189.61% |\n| | 2.441 ms | 0.054 ms | +4432.86% |\n| Jacobian | 1.997 ms | 0.074 ms | +2599.14% |\n| | 5.520 ms | 0.158 ms | +3397.66% |\n| Manipulability | 5.482 ms | 0.330 ms | +1559.99% |\n================================================================================\n```\n\n## Contributing\n\nWe welcome contributions! Our focus is on:\n* **Readability**: Clear algorithms over complex optimizations.\n* **Testability**: Comprehensive unit and integration tests.\n* **Documentation**: Well-documented code and examples.\n\n```bash\n# Development setup\ngit clone https://github.com/chaoyue/robotics-numpy\ncd robotics-numpy\npip install -e .[dev] # Or use uv for dependency management\n\n# Run tests\npytest\n\n# Check code quality\nruff check .\nmypy src/\n```\n\n## License\n\nMIT License - see [LICENSE](LICENSE) for details.\n\n## Acknowledgments\n\nInspired by Peter Corke's [Robotics Toolbox](https://github.com/petercorke/robotics-toolbox-python),\nwith a focus on minimal dependencies.\n",
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