Joint angle synergy-based humanoid robot motion generation with fascia-inspired nonlinear constraints

IF 1.9 4区 计算机科学 Q3 ROBOTICS
Robotica Pub Date : 2024-09-12 DOI:10.1017/s0263574724000961
Shiqi Yu, Yoshihiro Nakata, Yutaka Nakamura, Hiroshi Ishiguro
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引用次数: 0

Abstract

When generating simultaneous joint movements of a humanoid with multiple degrees of freedom to replicate human-like movements, the approach of joint synergy can facilitate the generation of whole-body robotic movement with a reduced number of control inputs. However, the trade-off of minimizing control inputs and keeping characteristics of movements makes it difficult to improve movement performance in a simple control manner. In this paper, we introduce an approach by connecting and constraining these joints. It is inspired by the fascia network of the human body, which constrains the whole-body movements of a human. Compared to when only joint synergy is used, the effectiveness of the proposed method is verified by calculating the errors of joint positions of generated movements and human movements. The paper provides a detailed exploration of the proposed method, presenting simulation-experimental results that affirm its effectiveness in generated movements that closely resemble human movements. Furthermore, we provide one possible method on how these concepts can be implemented in actual robotic hardware, offering a pathway to improve movement control in humanoid robots within their mechanical limitations.

利用筋膜启发的非线性约束生成基于关节角度协同作用的仿人机器人动作
在生成具有多个自由度的仿人机器人的同步关节运动以复制类似人类的运动时,关节协同的方法可以在减少控制输入的情况下促进全身机器人运动的生成。然而,在尽量减少控制输入和保持运动特性之间的权衡,很难通过简单的控制方式提高运动性能。在本文中,我们介绍了一种通过连接和约束这些关节的方法。它的灵感来源于人体的筋膜网络,该网络制约着人的全身运动。与只使用关节协同作用相比,通过计算生成运动和人体运动的关节位置误差,验证了所提方法的有效性。本文对所提出的方法进行了详细探讨,并展示了模拟实验结果,这些结果肯定了该方法在生成与人类动作十分相似的动作时的有效性。此外,我们还就如何在实际机器人硬件中实现这些概念提供了一种可行的方法,为在机械限制范围内改进仿人机器人的运动控制提供了一条途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Robotica
Robotica 工程技术-机器人学
CiteScore
4.50
自引率
22.20%
发文量
181
审稿时长
9.9 months
期刊介绍: Robotica is a forum for the multidisciplinary subject of robotics and encourages developments, applications and research in this important field of automation and robotics with regard to industry, health, education and economic and social aspects of relevance. Coverage includes activities in hostile environments, applications in the service and manufacturing industries, biological robotics, dynamics and kinematics involved in robot design and uses, on-line robots, robot task planning, rehabilitation robotics, sensory perception, software in the widest sense, particularly in respect of programming languages and links with CAD/CAM systems, telerobotics and various other areas. In addition, interest is focused on various Artificial Intelligence topics of theoretical and practical interest.
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