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Related Experiment Video

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Horizon: A Trajectory Optimization Framework for Robotic Systems.

Francesco Ruscelli¹, Arturo Laurenzi¹, Nikos G Tsagarakis¹

¹Humanoids and Human Centered Mechatronics (HHCM), Istituto Italiano di Tecnologia (IIT), Genova, Italy.

Frontiers in Robotics and AI

|August 1, 2022

Summary

This summary is machine-generated.

Horizon is an open-source framework simplifying robot motion generation through trajectory optimization. It offers a flexible Python API for complex dynamic movements, validated with diverse robotic platforms.

Keywords:

legged robotics locomotion motion planning non-linear programming (NLP)trajectory optimization

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Area of Science:

Robotics
Control Systems
Computational Science

Background:

Trajectory optimization is crucial for dynamic motion generation in robotics.
Existing frameworks may lack flexibility or user-friendliness for complex robotic systems.

Purpose of the Study:

Introduce Horizon, an open-source framework for robotic trajectory optimization.
Provide a user-friendly Python API for designing complex robot motions.
Enhance flexibility and customization for diverse robotic tasks.

Main Methods:

Horizon utilizes direct simultaneous methods to convert trajectory optimization problems into nonlinear programming problems.
The framework supports various state-of-the-art and custom-implemented solvers (GN-SQP, Iterative Linear-Quadratic Regulator).
It allows storing solutions for warm-starting and re-sampling while ensuring dynamic feasibility.

Main Results:

Horizon successfully generates complex and highly dynamic motions across multiple robotic platforms.
A case study demonstrates the framework's capability in generating a twisting jump for a quadruped robot.
The framework's modularity and Python API facilitate intuitive motion design and customization.

Conclusions:

Horizon offers a powerful, flexible, and user-friendly solution for robotic trajectory optimization.
The framework simplifies the generation of dynamic robot motions, enabling advanced applications.
Its validation across various platforms highlights its broad applicability in robotics research and development.