Multi-Robot Assembly of Deformable Linear Objects Using Multi-Modal Perception

TL;DR

This paper presents an integrated multi-modal perception and planning framework enabling robots to assemble deformable linear objects in industrial settings, addressing challenges of shape deformation and dynamic behavior.

Contribution

It introduces a comprehensive, object-centric approach combining visual and tactile data for DLO assembly, including bin picking, shape tracking, and multi-robot coordination.

Findings

Successful real-world multi-robot DLO assembly demonstrated

Effective shape and contact state tracking across stages

Framework applicable to complex industrial scenarios

Abstract

Industrial assembly of deformable linear objects (DLOs) such as cables offers great potential for many industries. However, DLOs pose several challenges for robot-based automation due to the inherent complexity of deformation and, consequentially, the difficulties in anticipating the behavior of DLOs in dynamic situations. Although existing studies have addressed isolated subproblems like shape tracking, grasping, and shape control, there has been limited exploration of integrated workflows that combine these individual processes. To address this gap, we propose an object-centric perception and planning framework to achieve a comprehensive DLO assembly process throughout the industrial value chain. The framework utilizes visual and tactile information to track the DLO's shape as well as contact state across different stages, which facilitates effective planning of robot actions. Our approach encompasses robot-based bin picking of DLOs from cluttered environments, followed by a coordinated handover to two additional robots that mount the DLOs onto designated fixtures. Real-world experiments employing a setup with multiple robots demonstrate the effectiveness of the approach and its relevance to industrial scenarios.