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，从现实世界中的自动驾驶汽车到自动驾驶汽车进行包装交付，再到辅助机器人，可以帮助日常活动中的残障人士。这些域中的自主机器人通常需要大量的计算资源来进行运动计划，这涉及通过避免障碍和完成任务的环境来计算机器人的安全运动。为了使低功率移动机器人能够实现其全部潜力，需要全新的算法和软件框架，以充分利用并行计算以及可通过云提供的仓库规模计算。 该项目旨在为云连接机器人的运动计划开发一个新的软件框架，该框架有效地使运动计划并行并行，并在机器人的嵌入式多核心处理器和多个基于云的计算服务器上分发计算。这项研究结合了来自计算机科学和工程的多个领域的想法，包括机器人技术，并行算法，高性能计算，运动计划和云计算。该项目在机器人的本地，使传统运动计划者完全利用低功率，嵌入式多项处理器。同时，该项目使机器人能够从基于云的资源请求计算时间，以显着增加可用于运动计划任务的计算能力，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自主完成运动计划挑战的新域中的任务，从而扩大了机器人对新的社会相关领域的适用性。该项目中开发的概念和软件正在整合到本科和研究生课程中，从机器人技术到高性能计算的主题。该项目的另一个目标是使用与云连接的机器人创建有趣的，动手的互动演示，以激发孩子们考虑STEM领域。