Trajectory Generation for Mobile Robots in a Dynamic Environment using Nonlinear Model Predictive Control
Paper i proceeding, 2021

This paper presents an approach to collision-free, long-range trajectory generation for a mobile robot in an industrial environment with static and dynamic obstacles. For the long-range planning a visibility graph together with A is used to find a collision-free path with respect to the static obstacles. This path is used as a reference path to the trajectory planning algorithm that in addition handles dynamic obstacles while complying with the robot dynamics and constraints. A Nonlinear Model Predictive Control (NMPC) solver generates a collision-free trajectory by staying close to the initial path but at the same time obeying all constraints. The NMPC problem is solved efficiently by leveraging the new numerical optimization method Proximal Averaged Newton for Optimal Control (PANOC). The algorithm was evaluated by simulation in various environments and successfully generated feasible trajectories spanning hundreds of meters in a tractable time frame.

Författare

Jonas Berlin

Student vid Chalmers

Georg Hess

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Anton Karlsson

Student vid Chalmers

William Ljungbergh

Student vid Chalmers

Ze Zhang

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Knut Åkesson

Ab Volvo Per-lage

Per-Lage Götvall

Chalmers, Elektroteknik, System- och reglerteknik, Automation

IEEE International Conference on Automation Science and Engineering

21618070 (ISSN) 21618089 (eISSN)

Vol. 2021-August 942-947

17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Lyon, France,

Ämneskategorier

Robotteknik och automation

Reglerteknik

Datavetenskap (datalogi)

DOI

10.1109/CASE49439.2021.9551644

Mer information

Senast uppdaterat

2021-10-28