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.

翻译：受限环境在可变形线性物体（DLO）操控的实际应用中普遍存在，此时DLO与机器人的运动均需受到约束。由于高度柔性的DLO、具有高自由度的双臂机器人以及三维复杂环境，该任务呈现高维度和高度约束特性，使得全局规划极具挑战性。此外，因DLO具有强非线性和多样性，规划所需的精确DLO模型往往难以获取，导致规划路径不可靠。本文聚焦于双臂机器人在受限环境中对DLO的全局移动与塑形，主要目标为：1）高效精准地完成该任务；2）实现对多种DLO的可泛化鲁棒操控。为此，我们提出一种采用适当DLO模型表征的全身规划与控制互补框架。首先，基于简化的DLO能量模型提出全局规划器，通过考虑完整系统状态与所有约束来高效求解可行方案，并规划更可靠的路径。其次，提出闭环操控方案以补偿建模误差并增强鲁棒性与精度，该方案融合模型预测控制器，基于自适应DLO运动模型实时调整机器人运动。核心创新在于，本框架能在无需精细模型辨识的条件下，高效求解多约束高维问题，并泛化至多种DLO。实验表明，与现有方法相比，本框架能以显著更高的效率、泛化性和可靠性完成更为复杂的任务。