# Robust Convex Model Predictive Control with collision avoidance guarantees for robot manipulators

## TL;DR

This paper introduces a convex, robust MPC approach for robot manipulators that guarantees collision avoidance and safe, fast motion in cluttered environments despite model uncertainties.

## Contribution

A novel convex MPC framework combining robust tube MPC and corridor planning for collision-free, high-speed robot manipulator control under uncertainties.

## Key findings

- Outperforms benchmark methods in handling higher model uncertainties.
- Enables faster motion while maintaining safety.
- Validated in simulation with a 6 DOF industrial robot.

## Abstract

Industrial manipulators are normally operated in cluttered environments, making safe motion planning important. Furthermore, the presence of model-uncertainties make safe motion planning more difficult. Therefore, in practice the speed is limited in order to reduce the effect of disturbances. There is a need for control methods that can guarantee safe motions that can be executed fast. We address this need 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, which we can solve fast, making our method practically useful. We demonstrate the efficacy of our method in a simulated environment with a 6 DOF industrial robot operating in cluttered environments with uncertainties in model parameters. We outperform benchmark methods, both in terms of being able to work under higher levels of model uncertainties, while also yielding faster motion.