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.

运动规划是机器人自主操作所需的基本能力。该项目增进了对机器人运动规划最先进方法的理解，并利用分析结果开发出越来越强大和实用的解决方案，这些解决方案与无人驾驶汽车和自动化制造等重要应用相关。基于现代采样机器人运动规划算法渐近收敛到最佳轨迹。然而，在实践中，它们的执行在有限的计算量之后停止。该项目在有限的计算时间而不是渐近分析后对这些流行方法的属性进行推理。 基于这一进展，开发了具有提高的实际计算效率和形式概率接近最优保证的方法。这些技术解决了一系列广泛的规划挑战，包括涉及重大动态的问题。这是一个特别重要的方向，因为很难应用具有近乎最优保证的现有解决方案。如果成功，这项工作将导致算法运动规划及其应用领域的范式转变，因为它提供了更有效的方法和更强有力的形式保证。 该项目包括传播研究成果并将其纳入课程的外展和教育活动。