Stability and self-organization of cooperative motions in multi-fingered robot hands

多指机器人手协作运动的稳定性和自组织

• 批准号: 13650292
• 负责人: SVININ M.m.
• 金额: $ 1.34万
• 依托单位: Bio-Mimetic Control Research Center, RIKEN
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2002
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13650292/
• 关键词: Force-dependent stability; Multi-fingered mechanisms; Compliance; Stiffness tensor; Grasping; Complexity; Stability; Reinforcement learning

Intelligent control of multiple-robots in cooperative motions requires sophisticated inter-arm andinter-finger coordination. While living creatures offer proof-of-concept, our understanding of how it can be realized in controlling robotic mechanisms is not complete. The main research goals were directed to understanding and realization of mechanisms for generating stable coordinated motion patterns, and also to developing principles of self-organization of cooperative motions. In the first part we studied configuration and force-dependent stability. To guarantee the system stability, one needs to understand how the arm compliance and regulation of the internal forces in musculo-skeletal system can be used in building stable motion patterns. Decomposing the motion and force redundancy, we have found some necessary and sufficient stability conditions for the internal force distributions and established criteria of stabilizability that can be used in designing impedance control schemes. In the second part we were dealing with decentralized coordination strategy and cooperative control. To avoid the complexity of the centralized control, we have developed a genetic-based machine learning technique, with reinforcement signals built upon the stability criteria. Initial experiments show the feasibility of this concept. Finally, we have studied a force distribution problem and found an optimality criterion for natural, human-like force generation.

多机器人协同运动的智能控制需要复杂的手臂间和手指间的协调。虽然生物提供了概念验证，但我们对如何在控制机器人机构中实现它的理解并不完整。研究的主要目标是理解和实现稳定的协调运动模式的产生机制，以及发展合作运动的自组织原理。在第一部分中，我们研究了结构和力相关稳定性。为了保证系统的稳定性，需要了解如何利用肌肉-骨骼系统的手臂顺应性和内力调节来构建稳定的运动模式。通过对运动冗余和力冗余的分解，得到了内力分布稳定的充分必要条件，并建立了可用于阻抗控制方案设计的稳定性判据。第二部分讨论了分散协调策略与协同控制。为了避免集中控制的复杂性，我们开发了一种基于遗传的机器学习技术，其强化信号建立在稳定性标准之上。初步实验证明了这一概念的可行性。最后，我们研究了一个力分布问题，并找到了一个自然的、类人的力生成的最优准则。

项目成果

M.Svinin, S.Hosoe, M.Uchiyama, Z.W.Luo: "Force-Dependent Stiffness of Mechanisms with Serial Structure"第20回日本ロボット学会学術講演会論文集. 1H-32 (2002)

M.Svinin、S.Hosoe、M.Uchiyama、Z.W.Luo：“串行结构机构的力相关刚度”日本机器人学会第 20 届学术会议论文集 1H-32 (2002)。

K.Ohta, M.Svinin, Z.W.Luo, S.Hosoe: "Dealing with constraints : Optimal trajectories of the constrained human arm movements"XVII IMEKO World Congress, Session TC-18 : Measurement in Human Functions. (in press). (2003)

K.Ohta、M.Svinin、Z.W.Luo、S.Hosoe：“处理约束：受约束的人体手臂运动的最佳轨迹”第十七届 IMEKO 世界大会，TC-18 会议：人类功能测量。

K. Ohta, M. Svinin, Z.W. Luo, S. Hosoe: "Optimal trajectory of human arm movements in constrained environments"12th Annual Meeting of the Society of Neural Control of Movement. No. E-02. (2002)

K. Ohta、M. Svinin、Z.W.

K.Yamada, M.Svinin, K.Ohkura, S.Hosoe, K.Ueda: "Two classifier system for reinforcement learning of motion patern"IFAC International Workshop on Mobile Robot Technology. 1-6 (2001)

K.Yamada、M.Svinin、K.Ohkura、S.Hosoe、K.Ueda：“运动模式强化学习的两个分类器系统”IFAC国际移动机器人技术研讨会。

K.Ohta, M.Sivnin, Z.W.Luo, S.Hosoe: "Dealing with Constraints : the Synthesis of Optimal Human Movements Constrained by the External Environment"4^<th> International Workshop on Emergent Synthesis. 183-188 (2002)

K.Ohta、M.Sivnin、Z.W.Luo、S.Hosoe：“处理约束：受外部环境约束的最佳人体运动的综合”第 4 届紧急综合国际研讨会。