An approach for automatic generation of the URDF file of modular robots from modules designed using SolidWorks

Feder, Maddalena; Giusti, Andrea; Vidoni, Renato

doi:10.1016/j.procs.2022.01.283

Cited by 9 publications

(1 citation statement)

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“…Each joint module in this paper has a standard input/output interface, using the characteristics of the URDF file. The "virtual linkage" can be defined by putting the input/output interface information in its corresponding URDF file [12]. For example, the T-joint module is shown in Figure 4, where the orange "virtual links" indicate the output interfaces and the blue "virtual links" indicate the input interfaces.…”

Section: Modelling Of Robotic Arm Modulesmentioning

confidence: 99%

A Modular Robotic Arm Configuration Design Method Based on Double DQN with Prioritized Experience Replay

Ding,

Tang,

Wan

et al. 2024

Symmetry

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The modular robotic arms can achieve desired performances in different scenarios through the combination of various modules, and concurrently hold the potential to exhibit geometric symmetry and uniform mass symmetry. Therefore, selecting the appropriate combination of modules is crucial for realizing the functions of the robotic arm and ensuring the elegance of the system. To this end, this paper proposes a double deep Q-network (DDQN)-based configuration design algorithm for modular robotic arms, which aims to find the optimal configuration under different tasks. First, a library of small modules of collaborative robotic arms consisting of multiple tandem robotic arms is constructed. These modules are described in a standard format that can be directly imported into the software for simulation, providing greater convenience and flexibility in the development of modular robotic arms. Subsequently, the DDQN design framework for module selection is established to obtain the optimal robotic arm configuration. The proposed method could deal with the overestimation problem in the traditional deep Q-network (DQN) method and improve the estimation accuracy of the value function for each module. In addition, the experience replay mechanism is improved based on the SumTree technique, which enables the algorithm to make effective use of historical experience and prevents the algorithm from falling into local optimal solutions. Finally, comparative experiments are carried out on the PyBullet simulation platform to verify the effectiveness and superiority of the configuration design method developed in the paper. The simulation results show that the proposed DDQN-based method with experience replay mechanism has higher search efficiency and accuracy compared to the traditional DQN scheme.

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Section: Modelling Of Robotic Arm Modulesmentioning

confidence: 99%