Robust convex model predictive control with collision avoidance guarantees for robot manipulators
File(s)
Accepted version
Author(s)
Wullt, Bernhard
Kohler, Johannes
Mattsson, Per
Norrlof, Mikael
Schon, Thomas B
Type
Journal Article
Abstract
Industrial manipulators typically operate in cluttered environments, where safe motion planning is critical. However, model uncertainties further complicate this task, which leads to conservative speed limits to reduce the influence of disturbances. Hence, there is a need for control methods that can guarantee safe motions that are executed fast. We address this by suggesting a novel model predictive control (MPC) solution for manipulators, where our two main components are a robust tube MPC and a corridor planning algorithm to obtain collision-free motion. Our solution results in a convex MPC formulation, which we can solve fast, making our method practically useful. We demonstrate the efficacy of our method in a simulated environment with a six DOF industrial robot operating in cluttered environments with uncertain model parameters. We outperform benchmark methods by tolerating higher levels of model uncertainty while achieving faster motion.
Date Issued
2026-07-03
Date Acceptance
2026-06-20
Citation
IEEE Transactions on Control Systems Technology, 2026
ISSN
1063-6536
Publisher
Institute of Electrical and Electronics Engineers
Journal / Book Title
IEEE Transactions on Control Systems Technology
Copyright Statement
Copyright © 2026 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published online
Date Publish Online
2026-07-03