NRI: FND: COLLAB: Design of dynamic multibehavioral robots: new tools to consider design tradeoff and enable more capable robotic systems

NRI：FND：COLLAB：动态多行为机器人的设计：考虑设计权衡并实现功能更强大的机器人系统的新工具

• 批准号: 1924303
• 负责人: Sam Burden
• 金额: $ 49.99万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924303&HistoricalAwards=false
• 关键词: NRI; FND; COLLAB; Design; dynamic

This project will create new techniques for designing robots that perform multiple dynamic behaviors. Currently, most robots can execute a limited set of behaviors like picking-and-placing objects or walking and running over level ground. To expand a robot's behavioral repertoire, it is standard practice to simply combine existing robots; for instance, attaching a robot arm to a wheeled or legged robot base produces a robot that can both move around and pick-and-place objects. This approach to design is expedient, but has obvious drawbacks: first, the resulting designs may be impractically large or expensive; second, there is a limit to the number of separate robots that can be combined, limiting the combined robot's behavioral repertoire. It would be better for robots to maximally re-using existing parts -- for instance, a single limb could be used both as a leg and an arm as is common in the animal kingdom -- but such robots are much harder to create because the relationship between design and behavior is complex. A new paradigm of design for multi-behavior would produce robots that can help society in a wide range of applications. For instance, home assistance robots must operate in environments built for humans, and as such they must have the flexibility to travel upstairs, over clutter, dig through drawers, manipulate small objects, and more. The results of this project may enable machines that can be customized to the demands of the specific sets of behaviors needed, reducing cost, size, and complexity. The methods developed here will help to lower the barrier to entry for robotics research and development by making design of complex robots easier, opening the field to engineers and entrepreneurs who can expand the range of applications of robotic technology.To enable robot designers to build systems that are capable of multiple behaviors, this project seeks to create automated techniques that can re-use parts in different behaviors while reasoning about performance tradeoffs that emerge between use cases. To achieve this, the project will analyze the relationship between design and behavior for dynamic robots using physics-based reduced order models. These models will capture the behaviors of interest and reduce the complexity of the design search space. The local geometry of these relationships will allow for the analysis and synthesis of multibehavioral robots that exposes the tradeoffs between competing design objectives. This reformulated multiobjective optimization problem will allow a designer to work in the space of behavioral performance without having to consider each design parameter independently, resulting in a significantly reduced search space. These methods will allow for a robot's design to be customized to the task scenario, increasing the overall system efficiency and effectiveness. These results will be demonstrated and evaluated in a case study wherein the design of a commercially-available quadrupedal robot is customized to capably execute multiple dynamic behaviors to perform a fetching task in varied scenarios.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.

该项目将创建用于设计执行多种动态行为的机器人的新技术。目前，大多数机器人只能执行有限的行为，如拾取和放置物体或在平地上行走和奔跑。 为了扩展机器人的行为能力，标准的做法是简单地将现有的机器人联合收割机组合起来;例如，将机器人手臂连接到轮式或腿式机器人基座上，产生一个既可以四处移动又可以拾取和放置物体的机器人。 这种设计方法是有利的，但有明显的缺点：首先，最终的设计可能是不切实际的大或昂贵的;第二，有一个限制的数量单独的机器人可以组合，限制了组合机器人的行为剧目。 机器人最好最大限度地重复使用现有部件-例如，一个肢体可以用作腿和手臂，这在动物王国中很常见-但这样的机器人更难创造，因为设计和行为之间的关系很复杂。 多行为设计的新范式将产生能够在广泛应用中帮助社会的机器人。 例如，家庭辅助机器人必须在为人类构建的环境中运行，因此它们必须具有上楼、越过杂物、挖掘抽屉、操纵小物体等灵活性。 该项目的结果可能使机器能够根据所需的特定行为集的需求进行定制，从而降低成本，尺寸和复杂性。这里开发的方法将有助于降低进入机器人研究和开发的门槛，使复杂机器人的设计更容易，为工程师和企业家打开领域，他们可以扩大机器人技术的应用范围。为了使机器人设计师能够构建能够实现多种行为的系统，这个项目寻求创建自动化技术，可以在不同的行为中重用部件，同时推理用例之间出现的性能权衡。 为了实现这一目标，该项目将使用基于物理的降阶模型分析动态机器人的设计和行为之间的关系。 这些模型将捕获感兴趣的行为，并降低设计搜索空间的复杂性。 这些关系的局部几何形状将允许多行为机器人的分析和合成，暴露了相互竞争的设计目标之间的权衡。 这种重新制定的多目标优化问题将允许设计人员在行为性能的空间中工作，而不必独立地考虑每个设计参数，从而大大减少了搜索空间。这些方法将允许根据任务场景定制机器人的设计，从而提高整个系统的效率和有效性。 这些结果将在一个案例研究中进行展示和评估，其中一个商用四足机器人的设计是定制的，能够执行多种动态行为，在不同的场景中执行抓取任务。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。