Title Participants
"MPC-based Motion Planning for Autonomous Truck-Trailer Maneuvering" "Mathias Bos, Bastiaan Vandewal, Wilm Decré, Jan Swevers"
"Optimal and Reactive Control for Agile Drone Flight in Cluttered Environments" "Dries Dirckx, Mathias Bos, Wilm Decré, Jan Swevers"
"IMPACT: A Toolchain for Nonlinear Model Predictive Control Specification, Prototyping, and Deployment" "Alejandro Astudillo Vigoya, Wilm Decré, Jan Swevers, Joris Gillis" "We present IMPACT, a flexible toolchain for nonlinear model predictive control (NMPC) specification with automatic code generation capabilities. The toolchain reduces the engineering complexity of NMPC implementations by providing the user with an easy-to-use application programming interface, and with the flexibility of using multiple state-of-the-art tools and numerical optimization solvers for rapid prototyping of NMPC solutions. IMPACT is written in Python, users can call it from Python and MATLAB, and the generated NMPC solvers can be directly executed from C, Python, MATLAB and Simulink. An application example is presented involving problem specification and deployment on embedded hardware using Simulink, showing the effectiveness and applicability of IMPACT for NMPC-based solutions."
"An Optimal Open-Loop Strategy for Handling a Flexible Beam with a Robot Manipulator" "Shamil Mamedov, Alejandro Astudillo Vigoya, Daniele Ronzani, Wilm Decré, Jan Swevers"
"Anderson Accelerated Feasible Sequential Linear Programming" "David Kiessling, Alejandro Astudillo Vigoya, Pieter Pas, Panos Patrinos, Jan Swevers"
"Improved crest factor minimization of multisine excitation signals using nonlinear optimization" "András Retzler, Jan Swevers, Joris Gillis"
"Varying-Radius Tunnel-Following NMPC for Robot Manipulators Using Sequential Convex Quadratic Programming" "Alejandro Astudillo Vigoya, Goele Pipeleers, Joris Gillis, Wilm Decré, Jan Swevers"
"Tasho: A Python Toolbox for Rapid Prototyping and Deployment of Optimal Control Problem-Based Complex Robot Motion Skills" "Ajay Sathya, Alejandro Astudillo Vigoya, Joris Gillis, Wilm Decré, Goele Pipeleers, Jan Swevers" "We present Tasho (Task specification for receding horizon control), an open-source Python toolbox that facilitates systematic programming of optimal control problem (OCP)-based robot motion skills. Separation-of-concerns is followed while designing the components of a motion skill, which promotes their modularity and reusability. This allows us to program complex motion tasks by configuring and composing simpler tasks. We provide templates for several basic tasks like point-to-point and end-effector path-following tasks to speed up prototyping. Internally, the task's symbolic expressions are computed using CasADi and the resulting OCP is transcribed using Rockit. A wide and growing range of mature open-source optimization solvers are supported for solving the OCP. Monitor functions can be easily specified and are automatically deployed with the motion skill, so that the generated motion skills can be easily embedded in a larger control architecture involving higher-level discrete controllers. The motion skills thus programmed can be directly deployed on robot platforms using the C-code generation capabilities of CasADi. The toolbox has been validated through several experiments both in simulation and on physical robot systems. The open-source toolbox can be accessed at: https://gitlab.kuleuven.be/meco-software/tasho"
"A Simple Formulation for Fast Prioritized Optimal Control of Robots using Weighted Exact Penalty Functions" "Ajay Sathya, Wilm Decré, Goele Pipeleers, Jan Swevers"
"Position and Orientation Tunnel-Following NMPC of Robot Manipulators Based on Symbolic Linearization in Sequential Convex Quadratic Programming" "Alejandro Astudillo Vigoya, Joris Gillis, Wilm Decré, Goele Pipeleers, Jan Swevers" "The tunnel-following nonlinear model predictive control (NMPC) scheme allows to exploit acceptable deviations around a path reference. This is done by using convex-over-nonlinear functions as objective and constraints in the underlying optimal control problem (OCP). The convex-over-nonlinear structure is exploited by algorithms such as the generalized Gauss-Newton (GGN) method or the sequential convex quadratic programming (SCQP) method to reduce the computational complexity of the OCP solution. However, the {modeling effort and engineering time} required to implement these methods is high. We address the problem of reducing the {modeling effort} in the implementation of SCQP, focusing on a standard sequential quadratic programming (SQP) implementation where symbolic linearization is applied to the nonlinear part of the convex-over-nonlinear functions in the objective and constraints. The novelty of this paper is twofold. It introduces a novel operator that applies symbolic linearization in a transparent and easy way to solve nonconvex OCPs with the SCQP method, and introduces a meaningful representation of an orientation-tunnel for robotic applications by means of a convex-over-nonlinear constraint, which preserves the convexity exploitation by the SCQP method. The proposed technique is demonstrated in a tunnel-following task for a 7-degrees-of-freedom manipulator."