The control toolbox — An open-source C++ library for robotics, optimal and model predictive control

Giftthaler, Markus; Neunert, Michael; Stäuble, Markus; Buchli, Jonas

doi:10.1109/simpar.2018.8376281

Cited by 58 publications

(40 citation statements)

References 22 publications

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“…The complete iLQR routine with parallelized line search and the algorithmic differentiation tools are implemented in the open-source Control Toolbox (CT) [27]. In this work, we generalize the CT to handle continuous and discrete (e.g., as a result of Alg.…”

Section: B Software Implementationmentioning

confidence: 99%

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Trajectory Optimization With Implicit Hard Contacts

Carius

Ranftl

Koltun

et al. 2018

IEEE Robot. Autom. Lett.

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Section: B Software Implementationmentioning

confidence: 99%

“…We evaluate our dynamics model against the soft contact model proposed by Neunert et al [4], which was also used for TO and is openly accessible in [27]. An important initial observation is that naively using soft contacts with default settings and a discretization of 10 ms yield a highly unstable behavior for our monopod.…”

Section: A Soft Contact Comparisonmentioning

confidence: 99%

Trajectory Optimization With Implicit Hard Contacts

Carius

Ranftl

Koltun

et al. 2018

IEEE Robot. Autom. Lett.

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“…Both resulting motions are shown in the video attachment 2 . All source code as well as the model, solver settings and cost function weights are available within our open source 'Robotics and Optimal Control Toolbox' [33].…”

Section: ) Slq Examplesmentioning

confidence: 99%

Automatic differentiation of rigid body dynamics for optimal control and estimation

et al. 2017

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Many algorithms for control, optimization and estimation in robotics depend on derivatives of the underlying system dynamics, e.g. to compute linearizations, sensitivities or gradient directions. However, we show that when dealing with Rigid Body Dynamics, these derivatives are difficult to derive analytically and to implement efficiently. To overcome this issue, we extend the modelling tool 'RobCoGen' to be compatible with Automatic Differentiation. Additionally, we propose how to automatically obtain the derivatives and generate highly efficient source code. We highlight the flexibility and performance of the approach in two application examples. First, we show a Trajectory Optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model. Second, we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly moving obstacle in a go-to task by fast, dynamic replanning. This paper is an extended version of [1].

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“…Among this class, ACADOS is a C library that is computational efficient as well as flexible [12]. CT is a C++ library of a class of algorithms for robotic applications [13]. It has a number of modules including MPC and has been deployed for many real-time NMPC applications.…”

Section: A Nmpc Software Packagesmentioning

confidence: 99%

MATMPC - A MATLAB Based Toolbox for Real-time Nonlinear Model Predictive Control

Bruschetta

Picotti

Beghi

2019

2019 18th European Control Conference (ECC)

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In this paper we introduce MATMPC, an open source software built in MATLAB for nonlinear model predictive control (NMPC). It is designed to facilitate modelling, controller design and simulation for a wide class of NMPC applications. MATMPC has a number of algorithmic modules, including automatic differentiation, direct multiple shooting, condensing, linear quadratic program (QP) solver and globalization. It also supports a unique Curvature-like Measure of Nonlinearity (CMoN) MPC algorithm. MATMPC has been designed to provide state-of-the-art performance while making the prototyping easy, also with limited programming knowledge. This is achieved by writing each module directly in MATLAB API for C. As a result, MATMPC modules can be compiled into MEX functions with performance comparable to plain C/C++ solvers. MATMPC has been successfully used in operating systems including WINDOWS, LINUX AND OS X. Selected examples are shown to highlight the effectiveness of MATMPC.

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The control toolbox — An open-source C++ library for robotics, optimal and model predictive control

Cited by 58 publications

References 22 publications

Trajectory Optimization With Implicit Hard Contacts

Trajectory Optimization With Implicit Hard Contacts

Automatic differentiation of rigid body dynamics for optimal control and estimation

MATMPC - A MATLAB Based Toolbox for Real-time Nonlinear Model Predictive Control

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