Reactive Model Predictive Contouring Control for Robot Manipulators

TL;DR

This paper introduces a reactive model predictive control framework for robot manipulators that effectively avoids obstacles, singularities, and self-collisions in dynamic environments at high speed, improving safety and reliability.

Contribution

It develops a novel RMPCC method with Control Barrier Functions and efficient optimization techniques for real-time obstacle avoidance in robot path following.

Findings

Operates at 100 Hz with real-time performance

Outperforms state-of-the-art methods by a factor of 10

Successfully handles dynamic obstacles in real-world experiments

Abstract

This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-collisions in dynamic environments at 100 Hz. Many path-following methods rely on the time parametrization, but struggle to handle collision and singularity avoidance while adhering kinematic limits or other constraints. Specifically, the error between the desired path and the actual position can become large when executing evasive maneuvers. Thus, this paper derives a method that parametrizes the reference path by a path parameter and performs the optimization via RMPCC. In particular, Control Barrier Functions (CBFs) are introduced to avoid collisions and singularities in dynamic environments. A Jacobian-based linearization and Gauss-Newton Hessian approximation enable solving the nonlinear RMPCC problem at 100 Hz, outperforming state-of-the-art methods by a factor of 10. Experiments confirm that the framework handles dynamic obstacles in real-world settings with low contouring error and low robot acceleration.