OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control Article Swipe

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Christopher E. Mower , João Moura , Nazanin Zamani Behabadi , Sethu Vijayakumar , Tom Vercauteren , Christos Bergeles ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2301.13512

This paper presents OpTaS, a task specification Python library for Trajectory Optimization (TO) and Model Predictive Control (MPC) in robotics. Both TO and MPC are increasingly receiving interest in optimal control and in particular handling dynamic environments. While a flurry of software libraries exists to handle such problems, they either provide interfaces that are limited to a specific problem formulation (e.g. TracIK, CHOMP), or are large and statically specify the problem in configuration files (e.g. EXOTica, eTaSL). OpTaS, on the other hand, allows a user to specify custom nonlinear constrained problem formulations in a single Python script allowing the controller parameters to be modified during execution. The library provides interface to several open source and commercial solvers (e.g. IPOPT, SNOPT, KNITRO, SciPy) to facilitate integration with established workflows in robotics. Further benefits of OpTaS are highlighted through a thorough comparison with common libraries. An additional key advantage of OpTaS is the ability to define optimal control tasks in the joint space, task space, or indeed simultaneously. The code for OpTaS is easily installed via pip, and the source code with examples can be found at https://github.com/cmower/optas.

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Python (programming language) Computer science Workflow Model predictive control Robotics Task (project management) Programming language Trajectory optimization Software Source code Compiler Optimal control Software engineering Artificial intelligence Control (management) Robot Mathematical optimization Database Engineering Systems engineering Mathematics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2301.13512
PDF: https://arxiv.org/pdf/2301.13512
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DOI: https://doi.org/10.48550/arxiv.2301.13512

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Title: OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control

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Publication year: 2023

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Publication date: 2023-01-31

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Authors: Christopher E. Mower, João Moura, Nazanin Zamani Behabadi, Sethu Vijayakumar, Tom Vercauteren, Christos Bergeles

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Landing page: https://arxiv.org/abs/2301.13512

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Concepts: Python (programming language), Computer science, Workflow, Model predictive control, Robotics, Task (project management), Programming language, Trajectory optimization, Software, Source code, Compiler, Optimal control, Software engineering, Artificial intelligence, Control (management), Robot, Mathematical optimization, Database, Engineering, Systems engineering, Mathematics

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