BSF:2012166:A Framework for Composite Techniques in Motion Planning

BSF:2012166：运动规划中的复合技术框架

• 批准号: 1330789
• 负责人: Kostas Bekris
• 金额: $ 4万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1330789&HistoricalAwards=false
• 关键词: BSF; 2012166; Framework; Composite; Techniques

This project is funded as part of the United States-Israel Collaboration in Computer Science (USICCS) program. Through this program, NSF and the United States - Israel Binational Science Foundation (BSF) jointly support collaborations among US-based researchers and Israel-based researchers. This project aims to design a framework for the integration of advanced foundational methods from computational geometry with effective sampling-based methods from motion planning. This will allow the practical use of these techniques in important applications and the development of useful educational experiences. Motion planning, in its basic form, corresponds to the problem of finding a collision-free path for a robot in a workspace cluttered with static obstacles. It is important for many application domains, such as manufacturing and warehouse management, product assembly, surgical planning, architectural design, graphical animation, computer games, and computational biology. The collaboration of the US and Israeli researchers will impact the application areas through the development of novel efficient tools based on sound theory for motion planning of complex systems. Furthermore, the availability of these tools can have an impact in educational efforts in the areas of algorithms, computational geometry and robotics.Towards achieving these objectives, the investigators will implement and evaluate composite methods for motion planning, that lie at the intersection of computational geometry and sampling-based planning. In particular, the investigators will utilize foundational methods to compute compact motion planning representations that provide optimality guarantees, based in part on recent advances in summary of big data in other fields. The collaboration can also lead to advances in the area of multi-robot motion planning, by taking advantage of recent progress in combinatorial solvers and transferring these results in the continuous motion planning domain. Overall, the integrated framework will allow advances in geometry-based algorithms to be readily available to the motion planning community, especially in sampling entire low-dimensional manifolds of the configuration space instead of individual configurations, collision detection and space decomposition.

该项目是美国-以色列计算机科学合作（USICCS）计划的一部分。通过该计划，NSF和美国-以色列两国科学基金会（BSF）共同支持美国研究人员和以色列研究人员之间的合作。该项目旨在设计一个框架，用于将计算几何的高级基础方法与运动规划的有效采样方法相结合。这将使这些技术能够在重要的应用中得到实际应用，并发展有用的教育经验。 运动规划，在其基本形式，对应的问题，找到一个无碰撞路径的机器人在一个工作空间与静态障碍物杂乱。它对于许多应用领域都很重要，例如制造和仓库管理、产品装配、手术规划、建筑设计、图形动画、计算机游戏和计算生物学。美国和以色列研究人员的合作将通过开发基于复杂系统运动规划合理理论的新型高效工具来影响应用领域。此外，这些工具的可用性可以在算法，计算几何和robotics.Towards实现这些目标的教育工作的影响，研究人员将实施和评估运动规划的复合方法，这是在计算几何和采样为基础的规划的交叉点。特别是，研究人员将利用基础方法来计算提供最优性保证的紧凑运动规划表示，部分基于其他领域大数据总结的最新进展。 合作也可以导致多机器人运动规划领域的进步，利用组合求解器的最新进展，并将这些结果在连续运动规划域。总的来说，集成框架将允许在基于几何的算法的进步，可以随时提供给运动规划界，特别是在采样整个低维流形的配置空间，而不是个别配置，碰撞检测和空间分解。