airo-planner 0.0.4

Motion planning for single and dual robot arms.

airo-planner

Python package for single and dual robot arm motion planning.

Key motivation:

• 🔗 Provide unified interfaces for different motion planners and collision checkers, such as OMPL's powerful (but robot-agnostic) sampling-based planners and Drake's collision checking for robots.
• 🦾 Standardize and add other features taylored to robotic arm motion planning such as joint limits and planning to TCP poses.

Overview 🧾

Features: this packages provides two main things:

• 🤝 Interfaces: specify interfaces for robot arm motion planning
  • SingleArmPlanner
  • DualArmPlanner
• 🔌 Implementations: reliable and well-tested implementations of these interfaces.
  • OMPL for single and dual arm planning to joint configurations or TCP poses

Design goals:

• ⚓ Robustness and stability: provide an off-the-shelf motion planner that supports research by reliably covering most (not all) use cases at our labs, prioritizing dependability over niche, cutting-edge features.

• 🧩 Modularity and flexibility in the core components:

  • 🧭 Motion planning algorithms
  • 💥 Collision checker
  • 🔙 Inverse kinematics

• 🐛 Debuggability and transparency: many things can go wrong in motion planning, so we log generously and store debugging information (IK solutions, alternative paths) to troubleshoot issues.

• 🧪 Enable experimentation: Facilitate the benchmarking and exploration of experimental planning algorithms.

Planned features:

• Drake optimization-based planning
• Nvidia cuRobo-based planning

Getting started 🚀

See the getting started notebooks, where we set up:

• 🎲 OMPL for sampling-based motion planning
• 🐉 Drake for collision checking
• 🧮 ur-analytic-ik for inverse kinematics of a UR5e

Installation 🔧

airo-planner is available on PyPI and installable with pip:

pip install airo-planner

We add underlying motion planning libraries as optional dependencies. For example, to install OMPL alongside airo-planner:

pip install airo-planner[ompl]

Developer guide 🛠️

See the airo-mono developer guide. A very similar process and tools are used for this package.

Releasing 🏷️

See airo-models, releasing airo-planner works the same way.