This study evaluates the performance of classical and modern control methods for real-world Cable-Driven Parallel Robots (CDPRs), focusing on underconstrained systems with limited time discretization. A comparative analysis is conducted between classical PID controllers and modern reinforcement learning algorithms, including Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO). The results demonstrate that TRPO outperforms other methods, achieving the lowest root mean square (RMS) errors across various trajectories and exhibiting robustness to larger time intervals between control updates. TRPO's ability to balance exploration and exploitation enables stable control in noisy, real-world environments, reducing reliance on high-frequency sensor feedback and computational demands. Cable-Driven Parallel Robots (CDPR) have unique parameters, which means they can move heavy loads within a fairly large space.

Cable-Driven Parallel Robots (CDPRs) are increasingly used for load manipulation tasks involving predefined toolpaths with intermediate stops. At each stop, where the platform maintains a fixed pose and the motors keep the cables under tension, the system must evaluate whether it is safe to proceed by detecting anomalies that could compromise performance (e.g., wind gusts or cable impacts). This paper investigates whether anomalies can be detected using only motor torque data, without additional sensors. It introduces an adaptive, unsupervised outlier detection algorithm based on Gaussian Mixture Models (GMMs) to identify anomalies from torque signals. The method starts with a brief calibration period, just a few seconds, during which a GMM is fit on known anomaly-free data. Real-time torque measurements are then evaluated using Mahalanobis distance from the GMM, with statistically derived thresholds triggering anomaly flags. Model parameters are periodically updated using the latest segments identified as anomaly-free to adapt to changing conditions. Validation includes 14 long-duration test sessions simulating varied wind intensities. The proposed method achieves a 100% true positive rate and 95.4% average true negative rate, with 1-second detection latency. Comparative evaluation against power threshold and non-adaptive GMM methods indicates higher robustness to drift and environmental variation.

In cable driven parallel robots (CDPRs), the payload is suspended using a network of cables whose length can be controlled to maneuver the payload within the workspace. Compared to rigid link robots, CDPRs provide better maneuverability due to the flexibility of the cables and consume lesser power due to the high strength-to-weight ratio of the cables. However, amongst other things, the flexibility of the cables and the fact that they can only pull (and not push) render the dynamics of CDPRs complex. Hence advanced modelling paradigms and control algorithms must be developed to fully utilize the potential of CDPRs. Furthermore, given the complex dynamics of CDPRs, the models and control algorithms proposed for them must be validated on experimental setups to ascertain their efficacy in practice. We have recently developed an elaborate experimental setup for a CDPR with three cables and validated elementary open-loop motion planning algorithms on it. In this paper, we describe several aspects of the design and fabrication of our setup, including component selection and assembly, and present our experimental results. Our setup can reproduce complex phenomenon such as the transverse vibration of the cables seen in large CDPRs and will in the future be used to model and control such phenomenon and also to validate more sophisticated motion planning algorithms.

Nowadays, being fast and precise are key requirements in Robotics. This work introduces a novel methodology to tune the stiffness of Cable-Driven Parallel Robots (CDPRs) while simultaneously addressing the tension distribution problem. In particular, the approach relies on the Analytic-Centre method. Indeed, weighting the barrier functions makes natural the stiffness adaptation. The intrinsic ability to adjust the stiffness during the execution of the task enables the CDPRs to effectively meet above-mentioned requirements. The capabilities of the method are demonstrated through simulations by comparing it with the existing approach.

-- This paper introduces the Cable Robot Simulation and Control (CaRoSaC) Framework, which integrates a realistic simulation environment with a model-free reinforcement learning control methodology for suspended Cable-Driven Parallel Robots (CDPRs), accounting for the effects of cable sag. Our approach seeks to bridge the knowledge gap of the intricacies of CDPRs due to aspects such as cable sag and precision control necessities, which are missing in existing research and often overlooked in traditional models, by establishing a simulation platform that captures the real-world behaviors of CDPRs, including the impacts of cable sag. The framework offers researchers and developers a tool to further develop estimation and control strategies within the simulation for understanding and predicting the performance nuances, especially in complex operations where cable sag can be significant. Using this simulation framework, we train a model-free control policy rooted in Reinforcement Learning (RL). This approach is chosen for its capability to adaptively learn from the complex dynamics of CDPRs. The policy is trained to discern optimal cable control inputs, ensuring precise end-effector positioning. Unlike traditional feedback-based control methods, our RL control policy focuses on kinematic control and addresses the cable sag issues without being tethered to predefined mathematical models. We also demonstrate that our RL-based controller, coupled with the flexible cable simulation, significantly outperforms the classical kinematics approach, particularly in dynamic conditions and near the boundary regions of the workspace. The combined strength of the described simulation and control approach offers an effective solution in manipulating suspended CDPRs even at workspace boundary conditions where traditional approach fails, as proven from our experiments, ensuring that CDPRs function optimally in various applications while accounting for the often neglected but critical factor of cable sag. CDPRs have emerged as a powerful subset of parallel manipulators, offering enhanced flexibility due to the replacement of rigid links with flexible cables.

A wire-driven parallel robot is a type of robotic system where multiple wires are used to control the movement of a end-effector. The wires are attached to the end-effector and anchored to fixed points on external structures. This configuration allows for the separation of actuators and end-effectors, enabling lightweight and simplified movable parts in the robot. However, its range of motion remains confined within the space formed by the wires, limiting the wire-driven capability to only within the pre-designed operational range. Here, in this study, we develop a wire-driven robot, CubiX, capable of connecting to and utilizing the environment. CubiX connects itself to the environment using up to 8 wires and drives itself by winding these wires. By integrating actuators for winding the wires into CubiX, a portable wire-driven parallel robot is realized without limitations on its workspace. Consequently, the robot can form parallel wire-driven structures by connecting wires to the environment at any operational location.

E-commerce is increasingly multimedia-enriched, with products exhibited in a broad-domain manner as images, short videos, or live stream promotions. A unified and vectorized cross-domain production representation is essential. Due to large intra-product variance and high inter-product similarity in the broad-domain scenario, a visual-only representation is inadequate. While Automatic Speech Recognition (ASR) text derived from the short or live-stream videos is readily accessible, how to de-noise the excessively noisy text for multimodal representation learning is mostly untouched. We propose ASR-enhanced Multimodal Product Representation Learning (AMPere). In order to extract product-specific information from the raw ASR text, AMPere uses an easy-to-implement LLM-based ASR text summarizer. The LLM-summarized text, together with visual data, is then fed into a multi-branch network to generate compact multimodal embeddings. Extensive experiments on a large-scale tri-domain dataset verify the effectiveness of AMPere in obtaining a unified multimodal product representation that clearly improves cross-domain product retrieval.

Abstract-- Robot art presents an opportunity to both showcase and advance state-of-the-art robotics through the challenging task of creating art. Creating large-scale artworks in particular engages the public in a way that small-scale works cannot, and the distinct qualities of brush strokes contribute to an organic and human-like quality. Combining the large scale of murals with the strokes of the brush medium presents an especially impactful result, but also introduces unique challenges in maintaining precise, dextrous motion control of the brush across such a large workspace. In this work, we present the first robot to our knowledge that can paint architectural-scale murals with a brush. We create a hybrid robot consisting of a cable-driven parallel robot and 4 degree of freedom (DoF) serial manipulator to paint a 27m by 3.7m mural on windows spanning 2-stories of a building. We discuss our approach to achieving both the scale and accuracy required for brush-painting a mural through a combination of novel mechanical design elements, coordinated planning and control, and on-site calibration algorithms with experimental validations.

The Cable-Driven Parallel Robots (CDPRs) have gained significant attention due to their high payload capacity and large workspace. When deploying CDPRs in practice, one of the challenges is kinematic modeling. Unlike serial mechanisms, CDPRs have a simple inverse kinematics problem but a complex forward kinematics (FK) issue. Therefore, the development of accurate and efficient FK solvers has been a prominent research focus in CDPR applications. By observing the topology within CDPRs, in this paper, we propose a graph-based representation to model CDPRs and introduce CafkNet, a fast and general FK solver, leveraging Graph Neural Network (GNN). CafkNet is extensively tested on 3D and 2D CDPRs in different configurations, both in simulators and real scenarios. The results demonstrate its ability to learn CDPRs' internal topology and accurately solve the FK problem. Then, the zero-shot generalization from one configuration to another is validated. Also, the sim2real gap can be bridged by CafkNet using both simulation and real-world data. To the best of our knowledge, it is the first study that employs the GNN to solve FK problem for CDPRs.

This paper presents a design and automation environment to study the control trajectory for new CDPR architectures, for instance CDPRs with an unusual number of cables or different motor location in the robot frame. In order to test the environment capabilities, an architecture of a planar under-constrained CDPR was designed, simulated, and implemented using standard industrial hardware. Both the simulated model and industrial prototype were running the same trajectories to determine the time delay and the error position between them. The tests have demonstrated that the simulated model of the CDPR reproduces the trajectories of the equivalent industrial prototype with a maximum deviation of 0.35% under loading and different speed conditions, despite the time delays produced by the data transmission and the non-deterministic communication protocols used to connect the industrial automation controller with the simulated model. The results have shown that the environment is suitable for trajectory control and workspace analysis of new CDPR architectures under different dynamic conditions.