Multi-layered Safety of Redundant Robot Manipulators via Task-oriented Planning and Control

TL;DR

This paper presents a task-oriented planning and control framework for redundant robot manipulators that enhances safety in uncertain environments through multi-layered strategies, ensuring effective obstacle handling and disturbance resistance.

Contribution

It introduces a novel multi-layered safety approach combining MPC-based trajectory planning and proprioception-based collision reaction for redundant manipulators.

Findings

Effective handling of static and dynamic obstacles in simulations and hardware

Demonstrated disturbance resistance during manipulation tasks

Enhanced safety without compromising task efficiency

Abstract

Ensuring safety is crucial to promote the application of robot manipulators in open workspaces. Factors such as sensor errors or unpredictable collisions make the environment full of uncertainties. In this work, we investigate these potential safety challenges on redundant robot manipulators, and propose a task-oriented planning and control framework to achieve multi-layered safety while maintaining efficient task execution. Our approach consists of two main parts: a task-oriented trajectory planner based on multiple-shooting model predictive control (MPC) method, and a torque controller that allows safe and efficient collision reaction using only proprioceptive data. Through extensive simulations and real-hardware experiments, we demonstrate that the proposed framework can effectively handle uncertain static or dynamic obstacles, and perform disturbance resistance in manipulation tasks when unforeseen contacts occur.