aikido
A C++ library with Python bindings for robotic motion planning and decision making, integrated with DART and OMPL.
What is aikido?
AIKIDO is a C++ library with Python bindings designed for solving robotic motion planning and decision making problems. It integrates with DART for kinematics/dynamics and OMPL for motion planning to provide a comprehensive toolkit for robot autonomy. The library optionally works with ROS to enable execution on real robots.
Robotics researchers, engineers, and developers working on motion planning, robot control, and autonomous systems, particularly those using C++ or Python in academic or industrial settings.
Developers choose AIKIDO for its tight integration with established robotics tools like DART and OMPL, its performance as a C++ library with Python accessibility, and its focus on bridging algorithmic motion planning with practical robot execution.
Overview
Artificial Intelligence for Kinematics, Dynamics, and Optimization
Use Cases
Best For
- Developing motion planning algorithms for robotic arms or mobile robots
- Integrating kinematics and dynamics calculations into robotic control systems
- Prototyping robot behaviors using Python bindings for faster iteration
- Deploying motion planning solutions on real robots via ROS integration
- Academic research in robotic autonomy and decision making
- Building simulation environments for robot testing and validation
Not Ideal For
- Projects requiring simple, out-of-the-box installation without managing multiple dependencies like ROS and specific DART versions
- Applications demanding hard real-time performance for low-level robot control due to potential abstraction latency
- Teams working exclusively outside Ubuntu environments, given incomplete macOS support and lack of cross-platform instructions
- Small hobbyist projects where a lighter, more accessible motion planning library would suffice
Pros & Cons
Pros
Tightly integrated with DART for accurate kinematic and dynamic calculations, essential for realistic robot simulations and control, as highlighted in the README's core philosophy.
Leverages OMPL to provide a wide array of motion planning algorithms, making it adaptable to various robotic scenarios from research to deployment.
Optional ROS integration via aikido_ros packages enables execution on physical robots, bridging the gap between simulation and real-world application.
Python bindings allow for quick scripting and algorithm testing, facilitating iterative development without sacrificing C++ performance for core computations.
Cons
Explicitly marked as under heavy development in the README warning, leading to potential API instability, breaking changes, and incomplete features.
Requires managing multiple dependencies, specific versions (e.g., DART 6.8.5+), and ROS setup, which is time-consuming and error-prone, especially on non-Ubuntu systems.
Primarily tested on Ubuntu; macOS support is experimental or untested, and other platforms are not addressed, restricting usability in diverse environments.
Frequently Asked Questions
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