XPS: FULL: DSD: Parallel Motion Planning for Cloud-connected Robots

XPS：完整：DSD：云连接机器人的并行运动规划

• 批准号: 1533844
• 负责人: Ron Alterovitz
• 金额: $ 67.05万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533844&HistoricalAwards=false
• 关键词: XPS; FULL; DSD; Parallel; Motion

Robots are entering new domains, from self-driving vehicles on real-world streets, to autonomous aerial vehicles for package delivery, to assistive robots helping people with disabilities in their homes with daily activities. Autonomous robots in these domains often need extensive computational resources for motion planning, which involves computing a safe motion for a robot through an environment that avoids obstacles and accomplishes the task. To enable low-power mobile robots to achieve their full potential, new algorithms and software frameworks are needed that fully leverage parallel computation and the warehouse-scale computing available via the cloud. This project aims to develop a new software framework for motion planning for cloud-connected robots that effectively parallelizes motion planning and distributes the computation across the robot's embedded multicore processor and multiple cloud-based compute servers. This research combines ideas from multiple areas of computer science and engineering, including robotics, parallel algorithms, high-performance computing, motion planning, and cloud computing. Locally on the robot, the project parallelizes traditional motion planners to fully leverage low-power, embedded multicore processors. Simultaneously, the project enables the robot to request computation time from cloud-based resources to significantly increase the computing power available for the motion planning task, and thus increase the responsiveness and quality of motion plans. The new algorithms and software aim to enable robots to autonomously complete tasks in new domains where the challenge of motion planning is currently prohibitive, broadening the applicability of robots to new societally-relevant domains. The concepts and software developed in this project are being integrated into undergraduate and graduate courses taught across topics ranging from robotics to high-performance computing. Another goal of the project is to create fun, hands-on, interactive demonstrations using cloud-connected robots to inspire children to consider STEM fields.

机器人正在进入新的领域，从现实世界街道上的自动驾驶汽车，到用于包裹递送的自动飞行器，再到帮助残疾人在家中进行日常活动的辅助机器人。这些领域的自主机器人通常需要大量的计算资源来进行运动规划，这涉及到计算机器人在避开障碍物并完成任务的环境中的安全运动。为了使低功耗移动机器人充分发挥其潜力，需要新的算法和软件框架，充分利用并行计算和通过云提供的仓库规模计算。 该项目旨在开发一种用于云连接机器人运动规划的新软件框架，该框架可以有效地并行化运动规划，并将计算分布到机器人的嵌入式多核处理器和多个基于云的计算服务器上。这项研究结合了计算机科学和工程多个领域的想法，包括机器人、并行算法、高性能计算、运动规划和云计算。在机器人本地，该项目并行化传统的运动规划器，以充分利用低功耗嵌入式多核处理器。同时，该项目使机器人能够从基于云的资源请求计算时间，从而显着提高可用于运动规划任务的计算能力，从而提高运动计划的响应能力和质量。新的算法和软件旨在使机器人能够在目前运动规划的挑战令人望而却步的新领域中自主完成任务，从而将机器人的适用性扩大到新的社会相关领域。该项目开发的概念和软件正在被整合到从机器人到高性能计算等主题的本科生和研究生课程中。该项目的另一个目标是使用云连接机器人创建有趣的、动手实践的交互式演示，以激发孩子们考虑 STEM 领域。