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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| Vydáno v: | 2023 IEEE International Conference on Robotics and Automation (ICRA) s. 9118 - 9124 |
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IEEE
29.05.2023
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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 github.com/cmower/optas. |
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| 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 github.com/cmower/optas. |
| Author | Vercauteren, Tom Mower, Christopher E. Bergeles, Christos Behabadi, Nazanin Zamani Vijayakumar, Sethu Moura, Joao |
| Author_xml | – sequence: 1 givenname: Christopher E. surname: Mower fullname: Mower, Christopher E. email: christopher.mower@kcl.ac.uk organization: School of Biomedical Engineering & Imaging Sciences, King's College,London,UK – sequence: 2 givenname: Joao surname: Moura fullname: Moura, Joao organization: School of Informatics, University of Edinburgh,UK – sequence: 3 givenname: Nazanin Zamani surname: Behabadi fullname: Behabadi, Nazanin Zamani – sequence: 4 givenname: Sethu surname: Vijayakumar fullname: Vijayakumar, Sethu organization: School of Informatics, University of Edinburgh,UK – sequence: 5 givenname: Tom surname: Vercauteren fullname: Vercauteren, Tom organization: School of Biomedical Engineering & Imaging Sciences, King's College,London,UK – sequence: 6 givenname: Christos surname: Bergeles fullname: Bergeles, Christos organization: School of Biomedical Engineering & Imaging Sciences, King's College,London,UK |
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| Snippet | This paper presents OpTaS, a task specification Python library for Trajectory Optimization (TO) and Model Predictive Control (MPC) in robotics. Both TO and MPC... |
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| SubjectTerms | Codes Optimal control Planning Software libraries Source coding Task analysis Trajectory optimization |
| Title | OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control |
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