Towards biomimetic control of robotic or paralyzed limbs

实现机器人或瘫痪肢体的仿生控制

• 批准号: RGPIN-2014-05886
• 负责人: Galiana, Henrietta
• 金额: $ 2.7万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=651441
• 关键词: Towards; biomimetic; control; robotic; paralyzed

Our long-term objectives are to learn from biological movement and devise efficient and novel control strategies for artificial systems (robots, prostheses) and for assisted human control (FES, neural interfaces). To date, we have focused on two streams in parallel sub-objectives:* 1- New analytical tools applicable to biological movement systems in the difficult context of non-linear parametric fields, switched hybrid strategies and imposed topologies. Funded by NSERC in the past, we produced software innovations now supported by NSERC/ I2I: to create automated classification of response modes in switched systems and identification tools to estimate the dynamics of each mode despite switching characteristics. They are targeted for distribution to Neuroscientists, Neuro-otologists and Clinicians in the field.* 2- Novel models of biological control strategies for eye-head and ocular reflexes (funded by CIHR) based on the special nature (topology) of neural circuits in the brainstem and spinal cord. These models change the interpretation of data at both behavioural and neural levels, and suggest new protocols to test the integrity of motor systems in more natural mixed-sensory environments, as in daily life. This is only possible now because our algorithms can handle diverse conditions. More recently, explorations on arm reaching (funded by NSERC) predict that our control strategies used in eye-head coordination are consistent with primate arm or leg trajectories and the topology of the spinal cord. The result is much simpler control than typically used in the robotic literature - i.e. movement without planning. * All of these systems use network topologies similar in their spatial organization and in the use of sensorimotor fusion. This suggests a possible general control theory for all platforms, be they stacked and rotatory, or segmental limbs. So the long-term goal is: * TO FORMALIZE A BIOMIMETIC CONTROL STRATEGY FOR MOVING SYSTEMS THAT WILL ALLOW EXECUTION OF SIMPLE TASKS WITHOUT A-PRIORI TRAJECTORY PLANNIN

我们的长期目标是从生物运动中学习，为人工系统（机器人，假肢）和辅助人类控制（FES，神经接口）设计有效和新颖的控制策略。迄今为止，我们重点关注了并行子目标中的两个流：* 1-适用于非线性参数场、切换混合策略和强加拓扑结构的困难背景下的生物运动系统的新分析工具。在NSERC的资助下，我们开发了现在由NSERC/I2 I支持的软件创新：创建切换系统中响应模式的自动分类和识别工具，以估计每个模式的动态，尽管切换特性。它们的目标是分发给该领域的神经科学家，神经耳科医生和临床医生。2-基于脑干和脊髓中神经回路的特殊性质（拓扑结构）的眼-头和眼反射生物控制策略的新模型（由CIHR资助）。这些模型改变了行为和神经层面上对数据的解释，并提出了新的协议来测试运动系统在更自然的混合感觉环境中的完整性，如在日常生活中。这是现在唯一可能的，因为我们的算法可以处理不同的条件。最近，对手臂伸展的探索（由NSERC资助）预测，我们在眼头协调中使用的控制策略与灵长类动物手臂或腿的轨迹以及脊髓的拓扑结构是一致的。其结果是比机器人文献中通常使用的控制简单得多-即无需规划的移动。 * 所有这些系统在空间组织和感觉运动融合方面都使用类似的网络拓扑结构。这表明一个可能的一般控制理论的所有平台，无论是他们堆叠和旋转，或节段肢体。因此，长期目标是：* 为运动系统形成仿生控制策略，允许执行简单任务而无需优先轨迹规划