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

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...

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Vydané v:arXiv.org
Hlavní autori: Mower, Christopher E, Moura, João, Nazanin Zamani Behabadi, Vijayakumar, Sethu, Vercauteren, Tom, Bergeles, Christos
Médium: Paper
Jazyk:English
Vydavateľské údaje: Ithaca Cornell University Library, arXiv.org 31.01.2023
Predmet:
Control tasks
Libraries
Optimal control
Optimization
Parameter modification
Predictive control
Programming languages
Robotics
Source code
Specifications
Task space
Trajectory optimization
ISSN:2331-8422
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Abstract 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.
AbstractList 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.
Author Moura, João
Vercauteren, Tom
Mower, Christopher E
Bergeles, Christos
Vijayakumar, Sethu
Nazanin Zamani Behabadi
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SubjectTerms Control tasks
Libraries
Optimal control
Optimization
Parameter modification
Predictive control
Programming languages
Robotics
Source code
Specifications
Task space
Trajectory optimization
Title OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control
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