权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

RI: Small: A Novel Framework for Informed Manipulation Planning

RI：小型：知情操纵规划的新颖框架

基本信息

批准号：
2008720
负责人：
Lydia Kavraki
金额：
$ 42.5万
依托单位：
William Marsh Rice University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008720&HistoricalAwards=false
关键词：
RI Small Novel Framework Informed

项目摘要

Humans use many types of manipulation to accomplish daily tasks and easily sequence and execute pertinent actions. Robots are confined to very simple tasks that are often painstakingly broken down to an excruciating level of detail by humans who operate those robots. Today, more than ever, there is an urgent need to develop robots that reason about manipulation as seamlessly as humans do. Manipulation planning finds a sequence of actions that move the robot and the objects it interacts with from a start state to a goal state. In doing so, contacts are formed and broken, limits are imposed, and some high-level reasoning occurs:. Grasping, placing, regrasping, and rearranging objects often take place. The combination of manipulation planning with classical AI planning and formal methods will lead to the kind of behavior that is expected from a robot capable of cleaning a hospital room, performing tasks in an ICU, or assisting a recovering patient.This project advocates a novel, unifying framework for manipulation planning. It adopts a constraint-centric view, in particular using manifold constraints which define lower-dimensional subspaces, or modes, among which the robot must transition. The definition of transitions is also constraint-centric, defined by the combination of the constraints which define modes, and is only possible within the unified approach used to consider modes. The work depends on constructs from differential geometry and the use of multi-resolution search schemes. First, the project will generalize sampling-based algorithms for robot motion planning by decoupling the planning strategy from the satisfaction of constraints often found in manipulation. Second, the concept of a transition graph between modes will be defined and exploited during search to automatically find viable transitions between modes and produce manipulation plans. The work will start with a specific but general type of constraint, manifold constraints, and later expand to other types. The proposed research will investigate optimality and robustness guarantees provided by the proposed framework and will identify the limits of using constraints as a unifying construct in manipulation planning. The work will be supported by extensive experimentation in both simulation and realistic scenarios that involve several objects, cabinets, shelves, a mobile manipulator, and two stationary state-of-the-art manipulators.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.

人类使用多种类型的操作来完成日常任务，并轻松排序和执行相关操作。机器人只能完成非常简单的任务，而这些任务往往被操作机器人的人煞费苦心地分解成令人痛苦的细节。如今，我们比以往任何时候都更迫切需要开发出能像人类一样对操作进行无缝推理的机器人。操作规划找到将机器人及其与之交互的对象从开始状态移动到目标状态的一系列动作。在这样做的过程中，建立和打破联系，施加限制，并发生一些高级推理：。抓取、放置、再抓取和重新排列物体经常发生。将操作计划与经典的人工智能计划和正式方法相结合，将使机器人能够完成清扫病房、在ICU执行任务或协助康复患者的行为。本项目提倡一种新颖的、统一的操作规划框架。它采用以约束为中心的观点，特别是使用多种约束来定义低维子空间或模式，机器人必须在这些子空间或模式之间转换。转换的定义也是以约束为中心的，由定义模式的约束的组合来定义，并且只有在用于考虑模式的统一方法中才有可能。这项工作依赖于微分几何的构造和多分辨率搜索方案的使用。首先，该项目将推广基于采样的机器人运动规划算法，将规划策略与操作中经常发现的约束条件解耦。其次，在搜索过程中定义并利用模式间转换图的概念，自动找到可行的模式间转换并生成操作计划。这项工作将从一种特定但一般的约束类型开始，即多种约束类型，然后扩展到其他类型。提出的研究将调查所提出的框架提供的最优性和鲁棒性保证，并将确定在操作规划中使用约束作为统一结构的限制。这项工作将在模拟和现实场景中进行广泛的实验，包括几个物体、橱柜、货架、一个移动机械手和两个固定的最先进的机械手。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。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Object Reconfiguration with Simulation-Derived Feasible Actions

DOI：
10.1109/icra48891.2023.10160377
发表时间：
2023-02
期刊：
2023 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
0
作者：
Yiyuan Lee;Wil B. Thomason;Zachary K. Kingston;L. Kavraki
通讯作者：
Yiyuan Lee;Wil B. Thomason;Zachary K. Kingston;L. Kavraki

Robowflex: Robot Motion Planning with MoveIt Made Easy

Robowflex：使用 MoveIt 轻松进行机器人运动规划

DOI：
10.1109/iros47612.2022.9981698
发表时间：
2022
期刊：
IEEE
影响因子：
0
作者：
Kingston, Zachary;Kavraki, Lydia E.
通讯作者：
Kavraki, Lydia E.

A Sampling-based Motion Planning Framework for Complex Motor Actions

DOI：
10.1109/iros51168.2021.9636395
发表时间：
2021-09
期刊：
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
Shlok Sobti;Rahul Shome;Swarat Chaudhuri;L. Kavraki
通讯作者：
Shlok Sobti;Rahul Shome;Swarat Chaudhuri;L. Kavraki

Asymptotically Optimal Kinodynamic Planning Using Bundles of Edges

使用边束的渐近最优运动动力学规划