Unified Interaction Force Estimation Under Multiple Contact Modes for Cable-Driven Parallel Robots

Abstract

Interaction force estimation in cable-driven parallel robots (CDPRs) is challenging under multiple contact modes, where external contact may occur on either the moving platform or the cables. Since different interaction modes lead to distinct force transmission paths and model mismatches, the problem is inherently heterogeneous and difficult to address with conventional estimators. To tackle this issue, this paper proposes a unified model-data fusion framework for interaction force estimation in CDPRs. A model-based estimator is first used to provide an initial physically interpretable estimation, and an attention-guided learning module is then introduced for residual compensation, to better handle the discrimination characteristics across interaction modes. In addition, a dedicated data acquisition device is developed, and zero-bias compensation is performed to improve data quality. In this way, only a single estimation algorithm can adaptively capture mode-relevant features and achieve unified force estimation across multiple interaction modes. Real-robot experiments and comparison studies demonstrate that the proposed framework enables accurate and unified interaction force estimation under multiple contact modes.