Search Results (42)

Cable-driven parallel robots (CDPRs) are increasingly favored in rehabilitation, medical devices, and material transportation due to their flexible structure and large transmission distance. The CDPRs with a highly modular and flexible structure are usually easy to be quickly reorganized. It is important to study the dimension and shape optimization of the basis and moving platforms for rapidly reconstructing a high-performance CDPR. The influence of each parameter of CDPRs’ dimension and shape on performance is mutually coupled. Therefore, obtaining the global optimal result by simply superimposing each optimum parameter is usually difficult. To this end, the concepts of a constant stiffness space (CSS) and a cable-tension-constrained workspace (CTCW) and their calculation methods are introduced, and the CDPRs’ dimension and shape are optimized with the maximum CSS and CTCW volume as the optimization indicators. First, the response surface optimization model between CDPRs’ performance and multi-objective optimization parameters is established, taking into account the coupling relationship of each CDPR optimization parameter and the effect on performance, and it is solved by using the Latin hypercube design method. Then, the effect of CDPRs’ dimension and shape on performance is analyzed by using the response surface optimization model, and the CDPRs’ optimization dimensions are provided. Full article

Although the use of adaptive pulleys enhances the motion characteristics of cable-driven parallel robots (CDPRs), it significantly increases the complexity of the kinematics model. Conventional methods often fail to account for the influence of adaptive pulley motion on cable length variation, making it difficult to establish a precise kinematics model. To deal with the problem, this study presents a kinematic modeling and solution method for CDPRs, which incorporates adaptive pulley kinematics. First, the structural design of the CDPR driven by eight cables is analyzed. Then, the generalized kinematics model and the improved kinematics model with adaptive pulley considerations are established. Furthermore, a hybrid Levenberg–Marquardt and Genetic algorithm is proposed to achieve the efficient and high-precision solution of kinematics equations by combining the rapid global search and precise local optimization. Finally, the proposed method is validated through straight path simulation and elliptical path simulation. The simulation results indicate that the tracking accuracy of the end-effector is better than the 1 × 10⁻⁷ level for the proposed method. Full article

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 the 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. Full article

Cable-driven parallel robots (CDPRs) offer large workspaces with minimal infrastructure, but their control becomes difficult when the platform is under-constrained and sensing is limited. This paper investigates uncalibrated visual servoing (UVS) with a single monocular camera, asking whether simple global static Jacobians (GSJ) can be sufficient and how an adaptive Jacobian estimator behaves. Two platforms are evaluated: a three-cable (3C) platform and a redundant six-cable du-al-plane platform (RC). Motion-capture (MoCap) validation shows that redundancy improves stability and tracking by reducing platform tilt and making image errors correspond more directly to Cartesian motions. Across static and low-speed tracking tasks, GSJ proved reliable, while a baseline recursive least-squares (RLS) estimator without safety triggers was often unstable. These findings suggest that improving mechanical conditioning may be as important as adding algorithmic complexity, and that carefully estimated global models can suffice in practice. Limitations include the use of a single camera, laboratory conditions, and a baseline RLS variant; future work will evaluate event-triggered adaptation and higher-speed trajectories. Full article

In view of the limitations of traditional rigid joint robots, cable-driven parallel robots (CDPRs) have shown significant advantages such as wide working space and high payload-to-weight ratio by replacing rigid connectors with flexible cables. Therefore, CDPRs have received widespread attention in the academic community in recent years and been applied to many fields. This review systematically reviews and categorizes the research progress in related fields in the past decade, focusing on mechanical structure design, mainstream mathematical models, and typical planning and control algorithms. In terms of mechanical structure, the advantages and disadvantages of three types of mainstream configurations and their application scenarios are summarized in detail. As for mathematical models, the dynamic modeling methods and various disturbance compensation models are mainly sorted out, and their action mechanisms and inherent limitations are explained. In terms of planning and control, four main research directions are discussed in detail, and their core ideas, evolution context, and development prospects are deeply analyzed. Although significant results have been achieved in the field of CDPR research, it is still necessary to continue to explore the direction of configuration diversification and intelligent autonomy in the future. Full article

Parallel robots, including cable-driven parallel robots (CDPRs), are widely used due to their high stiffness, precision, and high dynamic performance. However, their multi-chain closed-loop architecture brings nonlinear, multi-degree-of-freedom coupled motion and sensitivity to geometric errors, which result in significant challenges in their modeling, error compensation, and control. The rise in machine learning technology has provided a promising approach to address these issues by learning complex relationships from data, enabling real-time prediction, compensation, and adaptation. This paper reviews the progress of typical applications of machine learning methods in parallel robots, covering four main areas: kinematic modeling, error compensation, trajectory tracking control, as well as other emerging applications such as design synthesis, motion planning, and CDPR fault diagnosis. The key technologies used, their implementation architecture, technical difficulties solved, performance advantages and applicable scope are summarized. Finally, the review outlines current challenges and future directions. It is proposed that hybrid learning physics modeling, transfer learning, lightweight deployment, and interdisciplinary collaboration will be the key directions for advancing the integration of machine learning and parallel robotic systems. Full article

The construction industry faces a growing need for automation to reduce costs, improve accuracy and productivity, and address labor shortages. One area that stands to benefit significantly from automation is panelized prefabricated building envelope retrofits, which can improve a building’s energy efficiency in heating and cooling interior spaces. In this paper, we propose using cable-driven parallel robots (CDPRs), which can effectively lift and handle large objects, to install these panels. However, implementing CDPRs presents significant challenges because of their nonlinear dynamics, complex trajectory planning, and precise control requirements. To tackle these challenges, this work focuses on a new application of established control and trajectory optimization theories in a CDPR simulation of a building envelope retrofit under real-world conditions. We first model the dynamics of CDPRs, highlighting the critical role of damping in system behavior. Building on this dynamic model, we formulate a trajectory optimization problem to generate feasible and efficient motion plans for the robot under operational and environmental constraints. Given the high precision required in the construction industry, accurately tracking the optimized trajectory is essential. However, challenges such as partial observability and external vibrations complicate this task. To address these issues, a Linear Quadratic Gaussian control framework is applied, enabling the robot to track the optimized trajectories with precision. Simulation results show that the proposed controller enables precise end effector positioning with errors under 4 mm, even in the presence of external wind disturbances. Through comprehensive simulations, our approach allows for an in-depth exploration of the system’s nonlinear dynamics, trajectory optimization, and control strategies under controlled yet highly realistic conditions. The results demonstrate the feasibility of CDPRs for automating panel installation and provide insights into their practical deployment. Full article

Unmanned Aerial Vehicle (UAV) aerial recovery is a challenging task due to the limited maneuverability of both the transport aircraft and the UAV, making it difficult to establish an effective capture connection in the airflow field. In previous studies, we proposed using a Cable-Driven Parallel Robot (CDPR) for active interception and recovery of UAVs. However, during the aerial recovery process, the CDPR is continuously subjected to aerodynamic loads, which significantly affect the stiffness characteristics of the CDPR. This paper conducts a stiffness analysis of a single cable and a CDPR in a flow field environment. Firstly, we derive the stiffness matrix of a single cable based on a model that considers aerodynamic loads. The CDPR is then divided into elements using the finite element method (FEM), and the stiffness matrix for each element is obtained. These element stiffness matrices are assembled to form the stiffness matrix of the CDPR system. Secondly, we analyze the stiffness distribution of a single cable at various equilibrium positions within a flow field environment. Aerodynamic loads were observed to alter the equilibrium position of the cable, thereby impacting its stiffness. The more the cable bends, the greater the reduction in its stiffness. We examine the stiffness distribution characteristics of the CDPR’s end-effector within its workspace and analyze the impact of varying flow velocities and different cable materials on the system’s stiffness. This research offers a methodology for analyzing the stiffness of CDPR systems operating in a flow field environment. Full article

Cable-driven parallel robots (CDPRs) have been gaining much attention due to their many advantages over traditional parallel robots or serial robots, such as their markedly large workspace and lightweight design. However, one of the main issues that needs to be urgently solved is the tension in the distribution of CDPRs due to two reasons. The first is that a cable can only be stretched but not compressed, and the other is the redundancy of the parallel robot. To address the problem, an optimization method for tension distribution is proposed in the paper. The structural design of the parallel robot is first discussed. The dynamics model of the parallel robot is established by the Newton–Euler method. Based on the minimum variance of cables’ tension, an optimization method of tension distribution is presented for the parallel robot. Furthermore, the tension extreme average term is introduced in the optimization method, and the firefly algorithm is applied to obtain the optimal solution for tension distribution. Finally, the proposed approach is tested in the simulation case where the end-effector of the robot moves in a circular motion. Simulation results demonstrate that the uniformity and continuity of tension are both outstanding for the proposed method. In contrast with traditional solving methods, the efficiency of this method is largely improved. Full article

Cable-driven parallel robots (CDPRs) offer significant advantages, such as the lightweight design, large workspace, and easy reconfiguration, making them essential for various spatial applications and extreme environments. However, despite their benefits, CDPRs face challenges, notably the uncertainty in terms of the post-reconstruction parameters, complicating cable coordination and impeding mechanism parameter identification. This is especially notable in CDPRs with redundant constraints, leading to cable relaxation or breakage. To tackle this challenge, this paper introduces a novel approach using reinforcement learning to drive redundant constrained cable-driven robots with uncertain parameters. Kinematic and dynamic models are established and applied in simulations and practical experiments, creating a conducive training environment for reinforcement learning. With trained agents, the mechanism is driven across 100 randomly selected parameters, resulting in a distinct directional distribution of the trajectories. Notably, the rope tension corresponding to 98% of the trajectory points is within the specified tension range. Experiments are carried out on a physical cable-driven device utilizing trained intelligent agents. The results indicate that the rope tension remained within the specified range throughout the driving process, with the end platform successfully maneuvered in close proximity to the designated target point. The consistency between the simulation and experimental results validates the efficacy of reinforcement learning in driving unknown parameters in redundant constraint-driven robots. Furthermore, the method’s applicability extends to mechanisms with diverse configurations of redundant constraints, broadening its scope. Therefore, reinforcement learning emerges as a potent tool for acquiring motion data in cable-driven mechanisms with unknown parameters and redundant constraints, effectively aiding in the reconstruction process of such mechanisms. Full article

Tendon–sheath structures are commonly utilized to drive surgical robots due to their compact size, flexibility, and straightforward controllability. However, long-distance cable tension estimation poses a significant challenge due to its frictional characteristics affected by complicated factors. This paper proposes a miniature tension sensor array for an endoscopic cable-driven parallel robot, aiming to integrate sensors into the distal end of long and flexible surgical instruments to sense cable tension and alleviate friction between the tendon and sheath. The sensor array, mounted at the distal end of the robot, boasts the advantages of a small size (16 mm outer diameter) and reduced frictional impact. A force compensation strategy was presented and verified on a platform with a single cable and subsequently implemented on the robot. The robot demonstrated good performance in a series of palpation tests, exhibiting a 0.173 N average error in force estimation and a 0.213 N root-mean-square error. In blind tests, all ten participants were able to differentiate between silicone pads with varying hardness through force feedback provided by a haptic device. Full article

Aerial recovery and redeployment can effectively increase the operating radius and the endurance of unmanned aerial vehicles (UAVs). However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing road-based and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-casting spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV’s flight trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simultaneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic force and cable tension on the spatial configuration of the cable are analyzed. Numerical computations yield the CDPR’s end-effector position error and cable-driven power consumption at discrete spatial points, and the results show that the position error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, considering the error distribution, power consumption distribution, and safety distance. The optimized CTLL and interception space position coordinates are determined through simulation, and comparative analysis with the initial condition indicates an 83% reduction in error, a 62.3% decrease in power consumption, and a 1.2 m increase in safety distance. This paper proposes a new design for a UAV aerial recovery system, and the analysis lays the groundwork for future research. Full article

This paper proposes a hybrid position–force control strategy for overconstrained cable-driven parallel robots (CDPRs). Overconstrained CDPRs have more cables (m) than degrees of freedom (n), and the idea of the proposed controller is to control n cables in length and the other $m-n$ ones in force. Two controller implementations are developed, one using the motor torque and one using the motor following-error in the feedback loop for cable force control. A friction model of the robot kinematic chain is introduced to improve the accuracy of the cable force estimation. Compared to similar approaches available in the literature, the novelty of the proposed control strategy is that it does not rely on force sensors, which reduces the hardware complexity and cost. The developed control scheme is compared to classical methods that exploit force sensors and to a pure inverse kinematic controller. The experimental results show that the new controller provides good tracking of the desired cable forces, maintaining them within the given bounds. The positioning accuracy and repeatability are similar those obtained with the other controllers. The new approach also allows an online switch between position and force control of cables. Full article

The human-centric approach is a leading trend for future production processes, and collaborative robotics are key to its realization. This article addresses the challenge of designing a new custom-made non-conventional machine or robot involving toolpath control (interpolated axes) with collaborative functionalities but by using “general-purpose standard” safety and motion control technologies. This is conducted on a non-conventional cable-driven parallel robot (CDPR). Safety is assured by safe commands to individual axes, known as safe motion monitoring functionalities, which limit the axis’s speed in the event of human intrusion. At the same time, the robot’s motion controller applies an override to the toolpath speed to accommodate the robot’s path speed to the limitations of the axes. The implementation of a new Pre-Warning Zone prevents unnecessary stops due to the approach of the human operator. The article also details a real experiment that validates the effectiveness of the proposed strategy. Full article

In this paper, a Cable-Driven Parallel Robot developed to automate repetitive and essential tasks in crop production in greenhouse and urban garden environments is introduced. The robot has a suspended configuration with five degrees-of-freedom, composed of a fixed platform (frame) and a moving platform known as the end-effector. To generate its movements and operations, eight cables are used, which move through eight pulley systems and are controlled by four winches. In addition, the robot is equipped with a seedbed that houses potted plants. Unlike conventional suspended cable robots, this robot incorporates four moving pulley systems in the frame, which significantly increases its workspace. The development of this type of robot requires precise control of the end-effector pose, which includes both the position and orientation of the robot extremity. To achieve this control, analysis is performed in two fundamental aspects: kinematic analysis and dynamic analysis. In addition, an analysis of the effective workspace of the robot is carried out, taking into account the distribution of tensions in the cables. The aim of this analysis is to verify the increase of the working area, which is useful to cover a larger crop area. The robot has been validated through simulations, where possible trajectories that the robot could follow depending on the tasks to be performed in the crop are presented. This work supports the feasibility of using this type of robotic systems to automate specific agricultural processes, such as sowing, irrigation, and crop inspection. This contribution aims to improve crop quality, reduce the consumption of critical resources such as water and fertilizers, and establish them as technological tools in the field of modern agriculture. Full article