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4th International Conference on cable-driven parallel robots (cableCon) as Part of the 15th IFToMM World Congress

作者： Rasheed, Tahir Long, Philip Marquez-Gamez, David Caro, Stephane Ecole Cent Nantes CNRS UMR 6004 Lab Sci Numer Nantes 1 Rue Noe F-44321 Nantes France Northeastern Univ Dept Elect & Comp Engn River Lab Boston MA 02115 USA IRT Jules Verne Chemin Chaffault F-44340 Bouguenais France CNRS UMR 6004 Lab Sci Numer Nantes 1 Rue Noe F-44321 Nantes France

ISBN: (纸本)9783030207519;9783030207502

A mobile cable-driven parallel robot (MCDPR) is a special type of Reconfigurable cable-driven parallel robot (RCDPR) composed of a classical cable-driven parallel robot (CDPR) mounted on multiple mobile bases. The additional mobility of the mobile bases allows such systems to autonomously modify their geometric architecture, and thus make them suitable for multiple manipulative tasks in constrained environments. Moreover, these additional mobilities make MCDPRs kinematically redundant. Therefore, the subject of this paper is to introduce a two stage path planning algorithm for MCDPRs. The first stage searches for a feasible and collision free path of mobile bases. The second stage deals with generating an optimal path of the moving-platform to displace it from an initial to a desired pose. The proposed algorithm is validated through simulation on a three degree-of-freedom (DoF) point mass moving-platform displaced by four cables with each cable carried by an independent mobile base.

关键词： mobile cable-driven parallel robot Reconfigurability Kinematic Redundancy Path Planning Wrench Analysis

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Online motion planning of mobile cable-driven parallel robots for autonomous navigation in uncertain environments

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COMPLEX & INTELLIGENT SYSTEMS 2024年第1期10卷 397-412页

作者： Xu, Jiajun Kim, Byeong-Geon Feng, Xiguang Park, Kyoung-Su Gachon Univ Dept Mech Engn 1342 Seongnamdaero Seongnam Si 461701 Gyeonggi Do South Korea

mobile cable-driven parallel robots (MCDPRs) offer expanded motion capabilities and workspace compared to traditional cable-driven parallel robots (CDPRs) by incorporating mobile bases. However, additional mobile bases introduce more degree-of-freedom (DoF) and various constraints to make their motion planning a challenging problem. Despite several motion planning methods for MCDPRs being developed in the literature, they are only applicable to known environments, and autonomous navigation in unknown environments with obstacles remains a challenging issue. The ability to navigate autonomously is essential for MCDPRs, as it opens up possibilities for the robot to perform a broad range of tasks in real-world scenarios. To address this limitation, this study proposes an online motion planning method for MCDPRs based on the pipeline of rapidly exploring random tree (RRT). The presented approach explores unknown environments efficiently to produce high-quality collision-free trajectories for MCDPRs. To ensure the optimal execution of the planned trajectories, the study introduces two indicators specifically designed for the mobile bases and the end-effector. These indicators take into account various performance metrics, including trajectory quality and kinematic performance, enabling the determination of the final following trajectory that best aligns with the desired objectives of the robot. Moreover, to effectively handle unknown environments, a vision-based system utilizing an RGB-D camera is developed, allowing for precise MCDPR localization and obstacle detection, ultimately enhancing the autonomy and adaptability of the MCDPR. Finally, the extensive simulations conducted using dynamic simulation software (CoppeliaSim) and the on-board real-world experiments with a self-built MCDPR prototype demonstrate the practical applicability and effectiveness of the proposed method.

关键词： mobile cable-driven parallel robot Autonomous navigation Rapidly exploring random tree Stability and kinematic performance Vision system

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Optimal sampling-based path planning for mobile cable-driven parallel robots in highly constrained environment

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COMPLEX & INTELLIGENT SYSTEMS 2023年第6期9卷 6985-6998页

作者： Xu, Jiajun Kim, Byeong-Geon Lu, Yuzhen Park, Kyoung-Su Gachon Univ Dept Mech Engn 1342 Seongnamdaero Seongnam Si 461701 Gyeonggi Do South Korea Michigan State Univ Dept Biosyst & Agr Engn E Lansing MI 48824 USA

mobile cable-driven parallel robots (MCDPRs) is a novel concept of cable-driven parallel robots (CDPRs) developed by mounting several mobile bases to discrete the conventional fixed frame. However, the additional mobile bases introduce more degree-of-freedom (DoF), thereby causing the kinematic redundancy. Moreover, mobile bases are susceptible to disturbances causing inconsistent performance. Hence, path planning of MCDPRs becomes a challenging issue due to various internal and external constraints involved. In this article, an optimization-based path planning method is proposed for MCDPRs in highly constrained environments with considering kinematic stability. The proposed approach quickly generates feasible paths for coupled mobile bases by using the developed multi-agent rapidly exploring random tree (MA-RRT). In this process, the tree can share information through the heuristics method to optimize the path, and the adaptive sampling strategy is thus proposed to increase the tree growth efficiency by self-adjusting sampling space. Moreover, the developed dynamic control checking method (DCC) is integrated with MA-RRT to smooth the path and the kinodynamic constraints of mobile bases can be satisfied. To generate the path of the end-effector, two performance metrics are designed considering the kinematic and stability of the MCDPR. Based on the performance metrics, the grid-based search method is developed to generate the path for the end-effector. Finally, the convincing performance of the proposed method is revealed through the dynamic simulation software (CoppeliaSim) and real-world experiments based on a self-built MCDPR prototype.

关键词： mobile cable-driven parallel robot Rapidly exploring random tree Stability and kinematic performance Optimal sampling-based path planning

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A collaborative path planning method for mobile cable-driven parallel robots in a constrained environment with considering kinematic stability

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COMPLEX & INTELLIGENT SYSTEMS 2023年第5期9卷 4857-4868页

作者： Xu, Jiajun Kim, Byeong-Geon Park, Kyoung-Su Gachon Univ Dept Mech Engn 1342 Seongnamdaero Seongnam Si 461701 Gyeonggi Do South Korea

mobile cable-driven parallel robot (MCDPR) is a variant of cable-driven parallel robots (CDPRs) by mounting several mobile bases to replace the conventional fixed frame. The novel modification of adding mobile bases leads MCDPRs being highly flexible and have great potential for complex environments. However, the issue of coupled mobile bases introduces actuated kinematic redundancies which present challenges for path planning. In this paper, we propose a collaborative path planning method for MCDPRs, and it allows the robot to deal with complex internal and external constraints in a high-dimensional state space efficiently. The proposed method quickly generates feasible paths for coupled mobile bases using the adaptive goal-biased rapidly exploring random tree (RRT) method, in which the adaptive sampling method is developed to enhance efficiency. Based on the feasible path of the mobile base, we proposed a grid-based search method to determine the position of the end-effector with considering the stability and kinematic performances. Furthermore, the planned paths are post-processed with the cubic splines to obtain continuous profiles for the robot. Finally, the proposed method is validated through the dynamic simulation software (CoppeliaSim) and experiments based on a MCDPR prototype with an eight-cable-driven parallel robot mounted on four mobile bases.

关键词： mobile cable-driven parallel robot Rapidly exploring random tree Stability and kinematic performance Path planning

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Nonlinear dynamic modeling of a mobile spatial cable-driven robot with flexible cables

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NONLINEAR DYNAMICS 2022年第4期108卷 3219-3245页

作者： Goodarzi, R. Korayem, M. H. Tourajizadeh, H. Nourizadeh, M. Iran Univ Sci & Technol Robot Res Lab Ctr Excellence Expt Solid Mech & Dynam Sch Mech Engn Tehran Iran Kharazmi Univ Dept Mech Engn Fac Engn Tehran Iran

In this paper, the modeling of a novel moving cable-driven robot is conducted considering the vibration of the cables in its nonlinear format. The robot has 6 DOFs, while the controlling input number is 12. Considering the fact that the elasticity of the cables is coupled with the dynamic model of the system, their vibration affects the robot's performance and accuracy. This paper aims to model the robot considering the cable's elasticity and study its effect on the robot performance. This study can be considered in designing the controller of tower cranes, decreasing the cable's swing, and increasing their stability. In order to cover the mentioned aim, the continuous vibration of the cables is modeled as a nonlinear system and added to the moving platform dynamics. Moreover, the differences between the nonlinear modeling of the cable's vibration and estimating them as a linear system are studied, and their related results are compared and analyzed. The correctness of modeling is shown by comparing the results with previous research, and the superiority of modeling the cable's vibration in its nonlinear format is verified by the aid of a series of simulation scenarios in MATLAB. Moreover, by conducting some experimental tests on the manufactured moving cable-driven robot of IUST, it is illustrated that modeling the cables in these robots as a nonlinear system results in more accurate results. It is shown that not only considering the cable's vibration is significant in analyzing the robot dynamic, but also it is shown that promoting the mentioned model into a nonlinear one increases the accuracy of the robot modeling, which sequentially can provide a stronger controller for stabilizing and controlling the end-effector within a predefined trajectory.

关键词： mobile cable-driven parallel robot Nonlinear cable vibrations Rayleigh-Ritz method Null-space method

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