Purpose

Human-like musculoskeletal robots can fulfill flexible movement and manipulation with the help of multi joints and actuators. However, in general, sophisticated structures, accurate sensors and well-designed control are all necessary for a musculoskeletal robot to achieve high-precision movement. How to realize the reliable and accurate movement of the robot under the condition of limited sensing and control accuracy is still a bottleneck problem. This paper aims to improve the movement performance of musculoskeletal system by bio-inspired method.

Design/methodology/approach

Inspired by two kinds of natural constraints, the convergent force field found in neuroscience and attractive region in the environment found in information science, the authors proposed a structure transforming optimization algorithm for constructing constraint force field in musculoskeletal robots. Due to the characteristics of rigid-flexible coupling and variable structures, a constraint force field can be constructed in the task space of the musculoskeletal robot by optimizing the arrangement of muscles.

Findings

With the help of the constraint force field, the robot can complete precise and robust movement with constant control signals, which brings in the possibility to reduce the requirement of sensing feedback during the motion control of the robot. Experiments are conducted on a musculoskeletal model to evaluate the performance of the proposed method in movement accuracy, noise robustness and structure sensitivity.

Originality/value

A novel concept, constraint force field, is proposed to realize high-precision movements of musculoskeletal robots. It provides a new theoretical basis for improving the performance of robotic manipulation such as assembly and grasping under the condition that the accuracy of control and sensory are limited.

中文翻译：

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构造肌肉骨骼机器人约束力场的结构变换

目的

类人肌肉骨骼机器人可以在多关节和执行器的帮助下实现灵活的运动和操纵。然而，一般来说，复杂的结构、精确的传感器和精心设计的控制都是肌肉骨骼机器人实现高精度运动所必需的。如何在传感和控制精度有限的情况下实现机器人可靠、准确的运动仍然是一个瓶颈问题。本文旨在通过仿生方法提高肌肉骨骼系统的运动性能。

设计/方法/方法

受神经科学中的会聚力场和信息科学中的环境吸引力区域两种自然约束的启发，作者提出了一种构造肌肉骨骼机器人约束力场的结构变换优化算法。由于刚柔耦合和可变结构的特点，可以通过优化肌肉排列在肌肉骨骼机器人的任务空间中构建约束力场。

发现

在约束力场的帮助下，机器人可以在恒定的控制信号下完成精确而稳健的运动，这为降低机器人运动控制过程中对传感反馈的要求带来了可能。在肌肉骨骼模型上进行了实验，以评估所提出的方法在运动精度、噪声鲁棒性和结构敏感性方面的性能。

原创性/价值

为了实现肌肉骨骼机器人的高精度运动，提出了一种新概念——约束力场。为在控制精度和感官精度受限的情况下提高机器人装配、抓取等操作性能提供了新的理论依据。