Publications

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 ...

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 ...

Finding a time-optimal trajectory through gates and tunnels is a difficult challenge in autonomous drone racing, especially in a fully autonomous context where the computations are all performed onboard. This paper presents an optimal control problem formulation to represent and solve the motion planning problem for a multirotor drone racing through a series of gates or tunnels, without a priori knowledge about the drone pose right in front of ...

The repetitive evaluation of computationally expensive functions in the objective and constraints represents a bottleneck in the solution of the underlying optimal control problem (OCP) of nonlinear model predictive controllers (MPC) for robot manipulators. We address this problem by exploiting the parallel evaluation of such functions within the execution of a first-order and a second-order OCP solution algorithm, such as the proximal averaged ...