X-ACTA: eXtended Analytic Center Tension distribution Algorithm for fixed and mobile cable-driven-parallel-robot

Domenico Dona'; Vincenzo Di Paola; Alberto Trevisani; Matteo Zoppi

cs.ROJul 9, 2026

X-ACTA: eXtended Analytic Center Tension distribution Algorithm for fixed and mobile cable-driven-parallel-robot

Authors: Domenico Dona', Vincenzo Di Paola, Alberto Trevisani, Matteo Zoppi

Organizations: Faculty of Engineering, Free University of Bozen-Bolzano, Via Bruno Buozzi 1, Bozen 39100, Italy · Department of Mechanical, Energy, Management and Transportation Engineering (DIME), University of Genova, Via alla Opera Pia 15, Genova 16143, Italy · Department of Management and Engineering (DTG), University of Padua, Stradella San Nicola 3, Vicenza 36100, Italy

Abstract

Steering Cable-Driven Parallel Robots (CDPRs) beyond their Wrench-Feasible Workspace (WFW) augments their capabilities in challenging scenarios such as during aggressive maneuvers or following a cable failure. In this context, although the determination of cable tensions is a well-studied topic, only a few approaches address these scenarios. Therefore, this paper introduces an extended version of the Analytic Center method as a criterion for selecting cable tensions outside the WFW while maintaining differentiability and including non-linear constraints. Notably, the proposed method maintains continuous and differentiable tension profiles, ensures fast real-time convergence to a unique solution, and, in contrast to other slack-based formulations, relegates wrench errors to a negligible area of the WFW. Its superiority in terms of smoothness and wrench error is confirmed via Pareto dominance with respect to the leading state-of-the-art method. Lastly, the effectiveness of the method is demonstrated through numerical experiments.

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Jul 31, 2026 · cs.ROJ/K move · Enter open · S save

Generalizing deep reinforcement learning across cable-driven parallel robot configurations with actuator-level policies

Abir Bouaouda, Mohamed Boutayeb, François Charpillet, Dominique Martinez+1

CRAN, UIR · Université de Lorraine, INRIA, CNRS, LORIA, Nancy, F-54000, France · International University of Rabat, Rabat, 11103, Morocco · LARSEN · LORIA, ISM

Cable-driven parallel robots (CDPRs) present diverse configurations and complex control challenges, which can be addressed by deep reinforcement learning (DRL) by learning their nonlinear dynamics. However, DRL methods often require extensive training time, and the resulting policies do not generalize well to different robot configurations or varying numbers of actuators. In this article, we introduce a novel DRL approach for controlling CDPRs that does not depend on the specific robot configuration. Our method trains an actuator-level policy that controls each motor to achieve its target cable length, in contrast to conventional DRL approaches that learn to control the entire robot to reach a desired end-effector position. To the best of our knowledge, this is the first work to apply DRL to control CDPRs using an actuator-level policy. This approach offers two main advantages: (i) a single shared policy can be applied to any CDPR configuration, regardless of actuator count, and (ii) reliance on inverse kinematics, avoiding the more challenging forward kinematics problem. Training is performed in simulation, and the learned policy is successfully transferred to a real CDPR. Experimental results show that the actuator-level policy (ALP) surpasses traditional reinforcement learning methods in both robustness and precision. We further control a real 8-motor CDPR with 3D motion using a policy trained on a simulated 4-motor planar CDPR operating in 2D. This illustrates that the proposed method is applicable to any CDPR configuration, independent of actuator number or placement.

Iterative Reinforcement Learning Deep Reinforcement Learning Actuator

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Abstract

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