activity
20162022
most citedA COLREGs-Compliant Motion Planner for Autonomous Maneuvering of Marine Vessels in Complex Environments

7 citations · 16 across the 10 of their papers we have counts for

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math.OC20221 cited

Lift, Partition, and Project: Parametric Complexity Certification of Active-Set QP Methods in the Presence of Numerical Errors

Daniel Arnström, Daniel Axehill

When Model Predictive Control (MPC) is used in real-time to control linear systems, quadratic programs (QPs) need to be solved within a limited time frame. Recently, several parame…

math.OC20222 cited

BnB-DAQP: A Mixed-Integer QP Solver for Embedded Applications

Daniel Arnström, Daniel Axehill

We propose a mixed-integer quadratic programming (QP) solver that is suitable for use in embedded applications, for example, hybrid model predictive control (MPC). The solver is ba…

math.OC2021

A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive LDL' Updates

Daniel Arnström, Alberto Bemporad, Daniel Axehill

In this paper we present a dual active-set solver for quadratic programming which has properties suitable for use in embedded model predictive control applications. In particular,…

math.OC20217 cited

A COLREGs-Compliant Motion Planner for Autonomous Maneuvering of Marine Vessels in Complex Environments

Kristoffer Bergman, Oskar Ljungqvist, Jonas Linder +1

An enabling technology for future sea transports is safe and energy-efficient autonomous maritime navigation in narrow environments with other marine vessels present. This requires…

math.OC2020

An Optimization-Based Motion Planner for Autonomous Maneuvering of Marine Vessels in Complex Environments

Kristoffer Bergman, Oskar Ljungqvist, Jonas Linder +1

The task of maneuvering ships in confined environments is a difficult task for a human operator. One major reason is due to the complex and slow dynamics of the ship which need to…

math.OC2020

A Unifying Complexity Certification Framework for Active-Set Methods for Convex Quadratic Programming

Daniel Arnström, Daniel Axehill

In model predictive control (MPC) an optimization problem has to be solved at each time step, which in real-time applications makes it important to solve these optimization problem…