EAGER: Distributed Iterative Control of Soft Robotic Arms

这项探索性研究的早期概念拨款（EAGER）将创造一种新的方法来控制类似章鱼臂或象鼻的柔性机器人操纵器，并将通过实验验证这些新的控制规律。这个项目将探索两个创新方面。首先，控制权将被分散。也就是说，大量的传感器和执行器将分布在整个机械手中，而每个传感器收集的信息将仅用于控制附近的少数执行器。这种分布式控制策略避免了在整个结构中传输大量数据，并且不需要强大的中央计算机来处理大量数据。第二，控制律是迭代的。也就是说，软操纵器将学会在多次尝试中实现其目标。虽然所采取的方法并不是明确地基于生物学行为，但这两种特征都存在于使用顺从附属物操纵物体的动物身上。计算机模拟将用于研究新控制律在极其复杂的机器人上的性能，这些机器人有数百个单独可控的部分。物理实验将在有5到10个这样的部分的系统上进行。建模和控制方法将促进国家健康和经济繁荣，通过实现软多段机器人的可靠自主操作，提高其在众多应用中的可用性，包括制造，手术，搜索和救援，导航和个人家庭辅助。本项目将探索多节段软机器人系统的新型分布式规划和控制方法。与机器学习需要大量的训练数据相比，这种方法旨在创建具有高鲁棒性和可扩展性的直观且计算效率高的算法。具体的研究目标包括i)新颖的二维和三维目标到达和避障算法，仅使用局部传感器测量和信息处理；ii)可行的高保真动态模型来捕获非线性材料特性和压力动态；iii)用于软臂重力补偿和运动控制的鲁棒迭代学习控制算法；iv)使用物理多段软机械臂验证控制规律。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This EArly-concept Grant for Exploratory Research (EAGER) will create a new method of controlling compliant robot manipulators resembling octopus arms or elephant trunks, and will experimentally validate these new control laws. This project will explore two innovative aspects. First, control will be distributed. That is, a large number of sensors and actuators will be spread throughout the manipulator, and information gathered by each sensor will be used to control only a small number of nearby actuators. This distributed control strategy avoids the transmission of large amounts of data across the structure, and eliminates the need for a powerful central computer to process this flood of data. Second, the control law will be iterative. That is, the soft manipulator will learn to achieve its objectives over multiple attempts. Although the approach taken is not explicitly based on biological behavior, both these features are present in animals that manipulate objects using compliant appendages. Computer simulations will be used to study performance of the new control laws on extremely complex robots, with hundreds of individually controllable segments. Physical experiments will be conducted on systems with between five and ten such segments. The modeling and control approaches will advance the national health and economic prosperity, by enabling reliable autonomous operation of soft multi-segment robots, to increase their usability in numerous applications, including manufacturing, surgery, search and rescue, navigation, and personal home assistance.This project will explore novel distributed planning and control approaches for multi-segment soft robotic systems. In contrast to the large amounts of training data required by machine learning, this approach aims to create intuitive and computationally efficient algorithms with high robustness and scalability. Specific research goals include i) novel 2-D and 3-D goal reaching and obstacle avoidance algorithms, using only local sensor measurements and information processing, ii) feasible high-fidelity dynamic models to capture non-linear material properties and pressure dynamics, iii) a robust iterative learning control algorithm for gravity compensation and motion control of the soft arm, and iv) validation of the control laws using a physical multi-segment soft robotic arm.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.