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

• URL: http://arxiv.org/abs/2607.08265v1
• Date: Thu, 09 Jul 2026 09:07:56 GMT
• Title: X-ACTA: eXtended Analytic Center Tension distribution Algorithm for fixed and mobile cable-driven-parallel-robot
• Abstract summary: This paper introduces an extended version of the Analytic Center method as a criterion for selecting cable tensions outside the Wrench-Feasible Workspace (WFW)<n> Notably, the proposed method maintains continuous and differentiable tension profiles, ensures fast real-time convergence to a unique solution, and relegates wrench errors to a negligible area of the WFW.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Mechanical Analysis of Parachute Suspension Line Deployment with Binding Tapes Using PINN [8.412266694861163]
  This paper develops a physics-informed neural network (PINN) algorithm for tension prediction during line extraction and straightening.<n>It outperforms traditional integration methods in both computational efficiency and numerical accuracy.
  arXiv  Detail & Related papers  (2026-07-14T06:35:50Z)
• Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization [55.338784037660474]
  Multi- curvature reinforcement learning in domains requires balancing complex, non-fragile interference trade-offs between conflicting objectives.<n>In this paper we introduce a novel based real-time based real-time optimization on a robotic visual -- a proxy for monitoring ecosystems.
  arXiv  Detail & Related papers  (2026-05-12T21:32:57Z)
• Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline [7.967038299436285]
  We propose a deterministic adjoint matching framework that formulates human preference alignment for flow-based generative models.<n>Building on this perspective, we introduce a truncated adjoint scheme that focuses on the terminal portion of the trajectory.<n>We further generalize the framework beyond standard KL-based regularization, allowing more flexible trade-offs between alignment strength and distributional preservation.
  arXiv  Detail & Related papers  (2026-05-07T17:12:47Z)
• Regularized Centered Emphatic Temporal Difference Learning [14.415762410365106]
  Off-policy temporal-difference (TD) learning with function approximation faces a structural tradeoff among stability, projection geometry, and variance control.<n>Emphatic TD (ETD) improves the off-policy projection geometry through follow-on emphasis, but the follow-on trace can have high variance.<n>We propose emphRegularized Emphatic Temporal-Difference Learning (RETD), which preserves the follow-on trace and regularizes only the auxiliary centering recursion.
  arXiv  Detail & Related papers  (2026-05-02T10:32:24Z)
• Accelerated Spline-Based Time-Optimal Motion Planning with Continuous Safety Guarantees for Non-Differentially Flat Systems [6.862015448582809]
  State-of-the-art approaches formulate spline-based motion planning as a single Control Problem (OCP)<n>By treating separation as an independent classification problem solvable via a linear system or quadratic program, the proposed method eliminates hyperplane parameters from the OCP variables.<n> Experimental validation demonstrates that this decoupled approach reduces trajectory times up to almost 60% compared to fully-rich environments.
  arXiv  Detail & Related papers  (2026-03-25T09:53:04Z)
• BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
  BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
  arXiv  Detail & Related papers  (2026-02-23T18:19:46Z)
• Nonlinear Predictive Control of the Continuum and Hybrid Dynamics of a Suspended Deformable Cable for Aerial Pick and Place [4.356242302111724]
  This paper presents a framework for aerial manipulation of an cable that combines a high-fidelity model based on partial differential equations (PDEs) with a reduced-order representation suitable for real-time control.<n>The framework enables real-time, dynamics-aware control of unmanned aerial vehicles (UAVs) carrying suspended flexible cables.
  arXiv  Detail & Related papers  (2026-02-19T09:38:32Z)
• Steerable Adversarial Scenario Generation through Test-Time Preference Alignment [58.37104890690234]
  Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems.<n>We introduce a new framework named textbfSteerable textbfAdversarial scenario textbfGEnerator (SAGE)<n>SAGE enables fine-grained test-time control over the trade-off between adversariality and realism without any retraining.
  arXiv  Detail & Related papers  (2025-09-24T13:27:35Z)
• The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward [57.56453588632619]
  A central paradox in fine-tuning Large Language Models (LLMs) with Reinforcement Learning with Verifiable Reward (RLVR) is the frequent degradation of multi-attempt performance.<n>This is often accompanied by catastrophic forgetting, where models lose previously acquired skills.<n>We argue that standard RLVR objectives lack a crucial mechanism for knowledge retention.
  arXiv  Detail & Related papers  (2025-09-09T06:34:32Z)
• ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification [51.07970070817353]
  An ideal time series classification (TSC) should be able to capture invariant representations.<n>Current methods are largely unguided, lacking the semantic direction required to isolate truly universal features.<n>We propose an end-to-end Energy-Regularized Information for Shift-Robustness framework to enable guided and reliable feature disentanglement.
  arXiv  Detail & Related papers  (2025-08-19T12:13:41Z)
• Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding [55.107555305760954]
  We propose a conceptually simple yet effective method that attributes forgetting to layer-wise parameter overwriting and the resulting decision boundary distortion. Our method achieves competitive accuracy performance, even with absolute superiority of zero exemplar buffer and 1.02x the base model.
  arXiv  Detail & Related papers  (2024-01-17T09:01:29Z)
• Frequency-based tension assessment of an inclined cable with complex boundary conditions using the PSO algorithm [5.674726559114232]
  This study aimed to propose a frequency-based method of cable tension identification considering the complex boundary conditions at the two ends of cables. The proposed approach was applied to the tension identification of the anchor span of a suspension bridge (Jindong Bridge) in China.
  arXiv  Detail & Related papers  (2021-08-11T04:07:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.