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 this https URL.
Abstract (translated)
本论文介绍了 OpTaS,一个任务指定 Python 库,用于机器人中的路径优化(TO)和模型预测控制(MPC)。TO 和 MPC 在控制方面越来越受到关注,特别是处理动态环境。尽管存在许多软件库来处理这些问题,但它们要么提供接口限制于特定的问题 formulation(例如 TracIK、CHOMP),要么在大而静态地指定在配置文件中(例如 EXOTica、eTaSL)。相反,OpTaS 允许用户在一个 Python 脚本中指定自定义非线性约束问题 formulation,并在执行期间修改控制器参数。库提供了多个开源和商业求解器(例如 IPOPT、SNOPT、KNITRO、SciPy)的接口,以促进与机器人现有工作流程的集成。OpTaS 通过深入比较与其他常见库的优势,强调了其进一步的优点。OpTaS 的另一个关键优势是能够在关节空间、任务空间或事实上同时定义最佳的控制任务。OpTaS 的代码可以通过 pip 轻松安装,源代码和示例可以在 this https URL 上找到。
URL
https://arxiv.org/abs/2301.13512
