Generalizable whole-body global manipulation of deformable linear objects by dual-arm robot in 3-D constrained environments

TL;DR

This paper presents a novel dual-arm robot framework for global manipulation of deformable linear objects in 3-D constrained environments, achieving high efficiency, robustness, and generalizability without detailed DLO models.

Contribution

It introduces a complementary planning and control framework that efficiently handles high-dimensional constraints and generalizes across various DLOs without complex model identification.

Findings

Successfully manipulates complex DLO tasks in constrained environments

Achieves higher efficiency and robustness compared to existing methods

Demonstrates generalization to different DLO types

Abstract

Constrained environments are common in practical applications of manipulating deformable linear objects (DLOs), where movements of both DLOs and robots should be constrained. This task is high-dimensional and highly constrained owing to the highly deformable DLOs, dual-arm robots with high degrees of freedom, and 3-D complex environments, which render global planning challenging. Furthermore, accurate DLO models needed by planning are often unavailable owing to their strong nonlinearity and diversity, resulting in unreliable planned paths. This article focuses on the global moving and shaping of DLOs in constrained environments by dual-arm robots. The main objectives are 1) to efficiently and accurately accomplish this task, and 2) to achieve generalizable and robust manipulation of various DLOs. To this end, we propose a complementary framework with whole-body planning and control using appropriate DLO model representations. First, a global planner is proposed to efficiently find feasible solutions based on a simplified DLO energy model, which considers the full system states and all constraints to plan more reliable paths. Then, a closed-loop manipulation scheme is proposed to compensate for the modeling errors and enhance the robustness and accuracy, which incorporates a model predictive controller that real-time adjusts the robot motion based on an adaptive DLO motion model. The key novelty is that our framework can efficiently solve the high-dimensional problem subject to multiple constraints and generalize to various DLOs without elaborate model identifications. Experiments demonstrate that our framework can accomplish considerably more complicated tasks than existing works, with significantly higher efficiency, generalizability, and reliability.