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RI: Small: Robot Motion Planning with an Experience Database

RI：小型：使用经验数据库进行机器人运动规划

基本信息

批准号：
1718478
负责人：
Lydia Kavraki
金额：
$ 50万
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：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718478&HistoricalAwards=false
关键词：
RI Small Robot Motion Planning

项目摘要

Motion planning is the problem of determining how to get the robot from one point to another. Ideally, robots should have past experiences, of their own and others, inform future actions to operate more robustly and improve their performance over time. Motion planning, as it is largely practiced today, focuses on solving one problem at a time and makes limited use of past history. The goal of this project is to transform the way robots plan their motions by learning to exploit similarities between different experiences and by creating strategies that can adapt to wide range of scenarios. The work will create a bridge between the motion planning community and the information retrieval community, potentially transforming both fields. Training opportunities for diverse students will be offered. All developed software is disseminated under an open source license and infrastructure will enable other researchers to use the experience databases and contribute to them.This project provides a two-pronged approach to transform motion planning using an experience database. First, hashing will be used on an environment to fetch roadmaps for similar environments from a database. A roadmap is a graph representing feasible motions for a robot. These fetched roadmaps will be then lazily composed and refined to allow the robot to plan efficiently in the current environment. The use of prior experience will be done in tandem with planning from scratch; the latter, if successful, can provide a path and add to the experience database. The second prong in the planned approach will be to maintain various performance characteristics of a library of motion planning algorithms. These characteristics will be then used to optimize algorithm performance and construct a portfolio of algorithms that is competitive across various problems. The overall framework will be implemented in the cloud.

运动规划是确定如何使机器人从一个点到达另一个点的问题。理想情况下，机器人应该有自己和他人过去的经验，为未来的行动提供信息，以更稳健地运行，并随着时间的推移提高它们的性能。运动规划，就像今天大部分的实践一样，专注于一次解决一个问题，对过去历史的利用有限。该项目的目标是通过学习利用不同经验之间的相似之处，并通过创建能够适应广泛场景的策略，来改变机器人计划运动的方式。这项工作将在运动规划社区和信息检索社区之间建立一座桥梁，可能会改变这两个领域。将为不同的学生提供培训机会。所有开发的软件都在开源许可下传播，基础设施将使其他研究人员能够使用经验数据库并为其做出贡献。这个项目提供了一个双管齐下的方法来转换运动规划使用经验数据库。首先，将在一个环境中使用散列，从数据库中获取类似环境的路线图。路线图是表示机器人可行运动的图形。然后，这些获取的路线图将被惰性地组合和改进，以允许机器人在当前环境中有效地进行规划。利用先前的经验将与从零开始的规划同时进行；后者，如果成功，可以提供路径并添加到经验数据库中。计划方法的第二个方面将是保持运动规划算法库的各种性能特征。然后，这些特征将用于优化算法性能，并构建在各种问题上具有竞争力的算法组合。整个框架将在云中实现。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions

使用局部 3D 工作空间分解学习采样分布以进行高维运动规划

DOI：
发表时间：
2022
期刊：
Proceedings of the International Conference on Robotics and Automation 2021
影响因子：
0
作者：
Chamzas, Constantinos;Kingston, Zachary;Quintero-Pena, Carlos;Shrivastava, Anshumali;Kavraki, Lydia E.
通讯作者：
Kavraki, Lydia E.

Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search

DOI：
10.1145/3183713.3196925
发表时间：
2018-05
期刊：
Proceedings of the 2018 International Conference on Management of Data
影响因子：
0
作者：
Yiqiu Wang;Anshumali Shrivastava;Jonathan Wang;Junghee Ryu
通讯作者：
Yiqiu Wang;Anshumali Shrivastava;Jonathan Wang;Junghee Ryu

Learning to Retrieve Relevant Experiences for Motion Planning

学习检索运动规划的相关经验

DOI：
10.1109/icra46639.2022.9812076
发表时间：
2022
期刊：
2022 International Conference on Robotics and Automation
影响因子：
0
作者：
Chamzas, Constantinos;Cullen, Aedan;Shrivastava, Anshumali;Kavraki, Lydia E.
通讯作者：
Kavraki, Lydia E.

MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

MotionBenchMaker：生成运动规划数据集并对其进行基准测试的工具

DOI：
10.1109/lra.2021.3133603
发表时间：
2022
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
Chamzas, Constantinos;Quintero-Pena, Carlos;Kingston, Zachary;Orthey, Andreas;Rakita, Daniel;Gleicher, Michael;Toussaint, Marc;Kavraki, Lydia E.
通讯作者：
Kavraki, Lydia E.

Using Local Experiences for Global Motion Planning

利用本地经验进行全球运动规划