EAGER: Provably Efficient Motion Planning After Finite Computation Time

EAGER：有限计算时间后可证明高效的运动规划

• 批准号: 1451737
• 负责人: Kostas Bekris
• 金额: $ 17.5万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451737&HistoricalAwards=false
• 关键词: EAGER; Provably; Efficient; Motion; Planning

Motion planning is a fundamental capability needed for robots to operate autonomously. This project advances the understanding of state-of-the-art methods for robot motion planning and uses results from analysis to develop increasingly more capable and practical solutions, which are relevant to important applications such as driverless cars and automated manufacturing.Modern sampling-based algorithms for robot motion planning asymptotically converge to optimal trajectories. In practice, however, their execution is stopped after a finite amount of computation. This project reasons about the properties of these popular methods after finite computation time instead of an asymptotic analysis. Based on this progress, methods are developed with improved practical computational efficiency and formal probabilistic near-optimality guarantees. These techniques address a wide set of planning challenges, including problems that involve significant dynamics. This is an especially important direction, for which it is harder to apply existing solutions with near-optimality guarantees. If successful, this work will result in a paradigm shift in algorithmic motion planning and its application domains, since it provides more efficient methods with stronger formal guarantees. This project includes outreach and educational activities to disseminate research results and integrate them into curriculum.

运动计划是机器人自主操作所需的基本能力。该项目促进了对机器人运动计划的最先进方法的理解，并使用分析的结果来开发越来越有能力和实用的解决方案，这些解决方案与重要应用相关，例如无人驾驶汽车和自动化的制造业。基于现代采样的机器人运动计划算法不合时宜地汇总到最佳轨迹。但是，实际上，在计算有限的计算之后，他们的执行停止了。该项目原因是在有限的计算时间之后而不是渐近分析之后这些流行方法的性质。 基于此进展，开发方法具有提高的实际计算效率和正式的概率近距离保证。这些技术应对广泛的计划挑战，包括涉及重大动态的问题。这是一个特别重要的方向，很难将现有解决方案应用于近距离保证。如果成功，这项工作将导致算法运动计划及其应用领域的范式转移，因为它提供了具有更有效的正式保证的更有效的方法。 该项目包括宣传和教育活动，以传播研究结果并将其整合到课程中。