arXiv subjects

Po-Yu Hsieh

Publications and source records attributed to Po-Yu Hsieh.

4 recordsLinked to original sources

arXiv2024

Visualization and Optimization of Continuum Robots: Integration of Lie Group Kinematics and Evolutionary Algorithm

Continuum robots, known for their high flexibility and adaptability, offer immense potential for applications such as medical surgery, confined-space inspections, and wearable devices. However, their non-linear elastic nature and complex kinematics present significant challenges in digital modeling and visualization. Identifying the modal shape coefficients of specific robot configuration often requires plenty of physical experiments, which is time-consuming and robot-specific. To address this issue, this research proposes a computational framework that utilizes evolutionary algorithm (EA) to simplify the coefficient identification process. Our method starts by generating datasets using Lie group kinematics and physics-based simulations, defining both ideal configurations and models to be optimized. With the deployment of EA solver, the deviations were iteratively minimized through two fitness objectives \textemdash mean square error of shape deviation (\(\text{MSE}_1\)) and tool center point (TCP) vector deviation (\(\text{MSE}_2\)) \textemdash to align the robot's backbone curve with the desired configuration. Built on the Computer-Aided Design (CAD) platform Grasshopper, this framework provides real-time visualization suitable for development of continuum robots. Results show that this integrated method achieves precise alignment and effective identification. Overall, the objective of this research aims to reduce the modeling complexity of continuum robots, enabling precise, efficient virtual simulation before robot programming and implementation.

cs.RO↗

arXiv2024

A Multimedia Framework for Continuum Robots: Systematic, Computational, and Control Perspectives

Continuum robots, which often rely on interdisciplinary and multimedia collaborations, have been increasingly recognized for their potential to revolutionize the field of human-computer interaction (HCI) in varied applications due to their adaptive, responsive, and flexible characteristics. Despite their promises, the lack of an integrated framework poses a significant limitation for both users and developers, resulting in inefficiency and complexity during preliminary developments. Thus, this paper introduces a unified framework for continuum robotic systems that addresses these challenges by integrating system architecture, dynamics computation, and control strategy within a computer-aided design (CAD) platform. The proposed method allows for efficient modeling and quick preview of the robot performance, and thus facilitating iterative design and implementation, with a view to enhancing the quality of robot developments.

cs.RO↗

arXiv2024

Adaptive Stiffness: A Biomimetic Robotic System with Tensegrity-Based Compliant Mechanism

Biomimicry has played a pivotal role in robotics. In contrast to rigid robots, bio-inspired robots exhibit an inherent compliance, facilitating versatile movements and operations in constrained spaces. The robot implementation in fabrication, however, has posed technical challenges and mechanical complexity, thereby underscoring a noticeable gap between research and practice. To address the limitation, the research draws inspiration from the unique musculoskeletal feature of vertebrate physiology, which displays significant capabilities for sophisticated locomotion. The research converts the biological paradigm into a tensegrity-based robotic system, which is formed by the design of rigid-flex coupling and a compliant mechanism. This integrated technique enables the robot to achieve a wide range of motions with variable stiffness and adaptability, holding great potential for advanced performance in ill-defined environments. In summation, the research aims to provide a robust foundation for tensegrity-based biomimetic robots in practice, enhancing the feasibility of undertaking intricate robotic constructions.

cs.RO↗

arXiv2024

Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function Identifications

The control and modeling of robot dynamics have increasingly adopted model-free control strategies using machine learning. Given the non-linear elastic nature of bionic robotic systems, learning-based methods provide reliable alternatives by utilizing numerical data to establish a direct mapping from actuation inputs to robot trajectories without complex kinematics models. However, for developers, the method of identifying an appropriate learning model for their specific bionic robots and further constructing the transfer function has not been thoroughly discussed. Thus, this research introduces a comprehensive evaluation strategy and framework for the application of model-free control, including data collection, learning model selection, comparative analysis, and transfer function identification to effectively deal with the multi-input multi-output (MIMO) robotic data.

cs.RO↗