生物启发式肌肉骨骼机器人灵巧结构与控制研究
钟汕林
2021-11-19
Pages149
Subtype博士
Abstract

灵巧性是人在运动过程中所展现的卓越性能之一,如何让机器人实现像人一样灵巧、柔顺的运动和操作,是机器人领域长期关注的核心问题。相比于传统的关节连杆型机器人,由肌肉、骨骼、运动神经构成的人体运动系统,既能产生瞬时爆发力实现快速运动,又能在精密操作任务中对力和动作进行精准控制。一方面,人的运动系统由高度冗余的关节和肌肉构成,在大脑发达的神经调控作用下,能够完成灵巧柔顺的动作。另一方面,肌肉骨骼的刚柔耦合特性,使人体既能保持被动柔顺性顺应环境变化,以利用环境中的有利因素辅助完成高难度的操作任务;又能通过肌肉间的协同作用调控系统刚度,以确保操作任务能够准确完成。因此,通过借鉴人体运动系统的结构特性与控制机理,研制生物启发式肌肉骨骼机器人系统,对于进一步提高机器人的灵巧运动和操作能力具有重要的科学意义。

 

近年来,脑科学、神经科学、人工智能和机器人等学科迅猛发展,为跨学科开展受人启发的类脑智能机器人研究提供了重要基础。但是,如何从海量神经机制中凝练有利于提升机器人性能的核心机制,以及如何将介观层面的神经机制进行信息化表达,形成可计算、可实现的软硬件系统,成为该交叉研究领域的重要难点。

 

本文基于脑科学与神经科学的研究成果,围绕生物启发式肌肉骨骼机器人的灵巧结构分析与运动控制优化开展研究,通过借鉴人体肌肉骨骼系统的柔顺特性和大脑运动协调的神经机理,探索人类利用高冗余、强耦合的运动系统实现灵巧运动的机制与方法。本文从结构上模拟人的肌肉骨骼运动系统,从机制上模拟脑与脊髓的调控原理,构建了新型肌肉骨骼机器人系统及其控制方法,为提升机器人的灵巧运动能力提供了新的思路。本文的创新点主要有:

 

1.基于人体肌肉骨骼系统的生物力学特性,建立肌肉功能量化计算方法,提出针对肌肉骨骼系统冗余特性的结构优化算法。人类的肌肉骨骼系统能够实现精度高、灵活性好、可靠性强的运动和操作,但肌肉骨骼系统的冗余特性为类人机器人的硬件设计和控制带来了巨大挑战。因此,本研究基于凸包顶点选择原理,提出了一种能够有效降低肌肉骨骼系统冗余肌肉数量的结构优化算法。本研究首先构建了肌肉骨骼系统的动力学模型,结合肌肉的力约束、状态依赖等力学特性,建立了量化描述肌肉功能的特征模型,针对肌肉特征向量的凸包特性提出了肌肉骨骼机器人结构优化算法,可在保持系统运动精度的条件下,删减冗余肌肉,得到结构简化的肌肉骨骼模型,并基于该模型搭建了包含11条仿生肌肉、6个自由度的类人上肢肌肉骨骼机器人系统。

 

2.借鉴大脑运动皮层的神经增益调制机理,提出一种生物启发式增益调制循环神经网络,有效解决了高冗余、强耦合肌肉骨骼系统的运动控制问题。大脑运动皮层能够在网络连接结构不变的条件下,通过神经调质调节神经元增益以产生丰富的瞬态响应,实现对复杂运动的快速调节,使人类能在动态环境中快速、灵活地完成运动。受大脑运动皮层在信息编码和运动指令调制中的机制启发,本研究提出了一种生物启发式增益调制循环神经网络,建立了模拟神经激素调节过程的学习算法和模拟小脑运动误差修正的优化方法,有效解决了高冗余、强耦合和强非线性的肌肉骨骼系统运动学习和运动泛化问题。本研究在类人上肢肌肉骨骼机器人模型和传统关节连杆型机器人系统中开展运动实验,证明了增益调制神经网络能够控制复杂机器人系统精确完成丰富的运动行为,有效提升了算法快速泛化和增量学习的能力。

 

3.受脊髓中间神经元群对肌肉的协同调控机理启发,提出一种通过优化肌肉分布位置建立约束力场以实现肌肉骨骼机器人高精度运动的新方法,设计了新型变结构肌肉骨骼机器人系统。传统机器人需要借助精密的本体与传感和精巧的控制才能实现高精度的运动。受神经科学领域发现的“肌肉收敛力场”和信息科学领域发现的“环境吸引域”两种自然“约束”的启发,本研究提出了在肌肉骨骼机器人系统中构建约束力场的变结构优化算法。针对刚柔耦合、结构易变的肌肉骨骼机器人系统,通过优化肌肉的分布位置,在肌肉骨骼机器人任务空间中构建具有强抗噪性的约束力场,使肌肉骨骼机器人系统可以利用恒定的控制信号实现精准、鲁棒的运动,从而降低了机器人运动控制过程中对传感反馈的要求,为实现本体、传感精度有限情况下的机器人高精度灵巧操作提供了理论基础。

Other Abstract

Dexterity is one of the most remarkable features of human movement. How to improve the motion ability of robots to realize human-like dexterous and compliant manipulation has long been a core problem in the field of robotics. Compared with traditional articulated robots, the human motor system that consists of muscle, tendon and skeleton, can not only produce explosive force for fast movement but also ensure the stable and accurate force in elaborate operation. On the one hand, due to the highly redundant joints and muscles, the human motor system can complete complex and flexible movements under the modulation of well-developed neural systems. On the other hand, the rigid-flexible coupling characteristics of the musculoskeletal system guarantee the passive flexibility of human body to adapt to variations of environments, so that the favorable factors in the environment are able to be leveraged to assist in completing difficult operations. In addition, the stiffness of the system can be regulated through the synergistic effect between muscles to ensure that the manipulation tasks can be completed accurately. Therefore, based on the structural characteristics and control mechanism of the human motor system, the development of bio-inspired musculoskeletal robots has great scientific significance for further improving the dexterous and compliant ability of robots.

 

In the past few years, the rapid developments of brain science, neuroscience, artificial intelligence and robotics have provided an important foundation for the interdisciplinary research on brain-inspired intelligent robotics. However, it is still a great challenge to integrate biological mechanisms into robotics. For this interdisciplinary research field, two main bottleneck problems are how to summarize the core mechanisms that are beneficial to improving robot performance from massive neural mechanisms, and how to build the computational models of mesoscopic neural mechanisms to form realizable software and hardware systems.

 

Based on the research results of brain science and neuroscience, this thesis focus on the dexterous structure analysis and motion control optimization of bio-inspired musculoskeletal robots. By referring to the dexterous structural characteristics of the human musculoskeletal system and neural mechanisms of the brain in movement coordination, we explored the principles of the human motor system in realizing accurate and flexible movement with highly redundant and coupling structure. New kinds of hardware platforms and control methods of musculoskeletal robots are developed in this thesis by mimicking the structure of the musculoskeletal system and neural mechanisms of the brain and spinal cord, which may bring in new inspiration for promoting the dexterity of the robot. The key innovations are listed as follows:

 

 

1. According to the biomechanical characteristics of human musculoskeletal system, a quantitative method for computing muscle function is established, and a structure optimization algorithm for the redundancy of the musculoskeletal system is proposed. With highly redundant joints and actuators, the musculoskeletal system of humans can fulfill movement and operation with high precision, flexibility and reliability. However, the redundancy of musculoskeletal system also brings great difficulties to the control and fabrication of human-like robots. Therefore, a structure optimization algorithm is proposed based on convex hull theory to effectively reduce the number of redundant muscles in this work. The dynamics model of musculoskeletal system is first constructed based on the biomechanical characteristics. Then, taking the strength limitation and state-dependent characteristics of muscle into consideration, a feature model for quantitatively described the muscle function is established. By applying the proposed structure optimization algorithm, the muscles corresponding to the vertexes of convex hull formed by the muscle feature vectors are reserved while the redundant muscles within the convex hull are deleted, and a simplified model with high-precision motion ability and less redundant muscles is obtained. Based on the simplified model, a hardware platform of robotic arm has been constructed, which consists of 6 degrees of freedom and 11 bionic pneumatic muscles.

 

2. Inspired by the neuronal gain modulation mechanism in motor cortex of the brain, a bio-inspired gain-modulated recurrent neural network is proposed to control a highly redundant, coupled and nonlinear musculoskeletal robot. Motor cortex can arouse abundant transient responses to generate complex movements with regulations of neuromodulators, while its architecture remains unchanged. This characteristic endows humans with flexible and robust abilities in adapting dynamic environments, which is exactly the bottleneck problem in the control of complex robots. In this work, inspired by the mechanisms of motor cortex in encoding information and modulating motor commands, a biologically plausible gain-modulated recurrent neural network is proposed, and a novel learning rule that mimics the mechanism of neuromodulators in regulating the learning process of the brain is put forward to train the network effectively. In addition, a new optimization algorithm inspired by the error-based movement correction mechanism in the cerebellum is proposed to control novel movements. Experiments are conducted on an upper extremity musculoskeletal model and a general articulated robot to perform goal-directed tasks. The results indicate that the gain-modulated neural network can effectively control a complex robot to complete various movements with high accuracy, and the proposed algorithms make it possible to realize fast generalization and incremental learning ability.

 

3. Inspired by the synergistic regulation mechanism of muscle by interneurons of spinal cord, a novel method for constructing constraint force field by optimizing the distribution of muscles is proposed, which can help the musculoskeletal robot realizing high-precision motion with low-precision control. In general, sophisticated structures, highly accurate sensors and well-designed control are all necessary for a traditional robot to achieve high-precision movement. In this work, inspired by two kinds of natural constraints, convergent force field of muscle found in neuroscience and attractive region in environment found in information science, we proposed a structure transforming optimization algorithm for constructing constraint force field in musculoskeletal robots. For the musculoskeletal robot with rigid-flexible coupling and variable structures, constrain force field with strong robustness to noise perturbations can be constructed in the task space of the musculoskeletal robot by optimizing the distribution of muscles. 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. Based on the formation principle of the constraint force field, a new kind of musculoskeletal robot with variable structure is designed. This work provides a theoretical basis for realizing high-precision manipulation of robots with limited control and sensory accuracy.

Keyword肌肉骨骼机器人 生物启发式算法 机器人灵巧运动 环境吸引域
Language中文
Sub direction classification智能机器人
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46632
Collection多模态人工智能系统全国重点实验室_机器人理论与应用
Recommended Citation
GB/T 7714
钟汕林. 生物启发式肌肉骨骼机器人灵巧结构与控制研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2021.
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