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OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control (2301.13512v1)

Published 31 Jan 2023 in cs.RO

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.

Citations (11)

Summary

  • The paper presents pyinvk as a flexible library that simplifies the formulation and solution of constrained inverse kinematics and model predictive control problems.
  • It features customizable problem setups with integration of multiple solvers and leverages CasADi for efficient automatic differentiation.
  • Real-world experiments on diverse robotic platforms, including ROS integration, demonstrate its competitive performance and ease of deployment.

PyInvK: A Library for Constrained Inverse Kinematics and Model Predictive Control

The paper presents pyinvk, a Python library designed to streamline the formulation and solution of inverse kinematics (IK) and model predictive control (MPC) problems. Inverse kinematics is essential for mapping task space goals, such as a robot's end-effector position, to corresponding joint space configurations. Given the widespread application of inverse kinematics across various robotic systems, efficient and flexible software solutions are paramount. This research provides a thorough examination of the proposed library, designed to fill existing gaps in the current landscape of IK solutions.

Overview

The pyinvk library introduces several features distinguishing it from existing software packages. Notably, it allows users to specify custom nonlinear constrained problem formulations within a single Python script, simplifying the prototyping and deployment processes. By leveraging Python's versatility, pyinvk affords an accessible interface for both beginners and advanced users. Furthermore, the integration with several open-source and commercial solvers (such as IPOPT, SNOPT, KNITRO, and Scipy) ensures it can address a wide spectrum of optimization problems with sufficient flexibility to adapt to varying needs.

A notable component of pyinvk is the use of CasADi, a framework for nonlinear optimization, which facilitates the implementation of derivatives of functions to arbitrary orders — crucial for high-fidelity IK solutions. The library also supports the Robot Operating System (ROS), a feature essential for real-world robotic applications.

Contributions and Implications

The contributions of this work are primarily encapsulated in the design and implementation of the pyinvk library, which boasts the following elements:

  • Customizable inverse kinematics problem formulations, including constraints and time horizons suitable for optimal control problem setups.
  • An extensible interface allowing seamless integration with various optimization solvers.
  • Demonstrated performance comparability to existing software solutions, with additional benefits surrounding ease of use and hardware deployment.

The significance of these contributions lies in the library's ability to facilitate the exploration and testing of innovative control and motion planning algorithms. Its ease of deployment makes it suitable for benchmarking, educational purposes, and practical robotics applications. Moreover, researchers can employ pyinvk to develop platform-independent problem formulations, broadening the potential application scenarios and driving future advancements in robot autonomy and intelligence.

Example Use-Cases and Experiments

The paper also provides detailed example use-cases demonstrating how common inverse kinematic problems can be implemented using pyinvk. These examples showcase the library’s simplicity in handling various optimization problems, including unconstrained, quadratic, and constrained nonlinear formulations, as well as optimization over time horizons. Demonstrations on hardware platforms such as Kuka and Nextage solidify the library's utility in real-world settings.

In experimental settings, pyinvk is evaluated against alternatives like TracIK and EXOTica, highlighting its capability to deploy nonlinear constrained optimization problems efficiently across different robotic systems. Figures within the paper illustrate solver durations, positional errors, and rotational errors, portraying comparable or improved performance relative to existing libraries.

Conclusion and Future Prospects

The pyinvk library represents an instrumental tool in the toolkit for software-based IK and MPC solutions, particularly marrying flexibility with ease of use. While the paper avoids boasting revolutionary ideas, it nonetheless positions pyinvk as a valuable asset for researchers, students, and industry professionals engaged with robotic motion planning and control tasks.

Anticipated future developments could include further extending the solver interface to incorporate emerging optimization techniques and further enhancing interoperability with robotic middleware platforms. As the field of AI and robotics progresses, libraries like pyinvk will play a crucial role in driving tangible advances within these domains, fostering innovation and practical implementations across diverse robotic applications.