Collision-free Motion Planning for Mobile Robots by Zero-order Robust Optimization-based MPC

TL;DR

This paper introduces a robust MPC method using zero-order optimization for collision-free mobile robot navigation, effectively handling process noise and ensuring convergence to reference trajectories in obstacle-free scenarios.

Contribution

It proposes a novel zero-order robust optimization scheme within MPC for collision avoidance, improving efficiency in disturbance management for mobile robots.

Findings

Effective collision-free trajectory tracking demonstrated on industrial robots

Zero-order robust optimization maintains convergence without obstacle presence

Method handles bounded process noise within ellipsoidal sets

Abstract

This paper presents an implementation of robust model predictive control (MPC) for collision-free reference trajectory tracking for mobile robots. The presented approach considers the robot motion to be subject to process noise bounded by ellipsoidal sets. In order to efficiently handle the evolution of the disturbance ellipsoids within the MPC, the zero-order robust optimization (zoRO) scheme is applied. The idea is to fix the disturbance ellipsoids within one optimization iteration and solve the problem repeatedly with updated disturbance ellipsoid trajectories. The zero-order approach is suboptimal in general. However, we show that it does not impair convergence to the reference trajectory in the absence of obstacles. The experiments on an industrial mobile robot prototype demonstrate the performance of the controller.