CAREER: SHF: Chimp: Algorithm-Hardware-Automation Co-Design Exploration of Real-Time Energy-Efficient Motion Planning

职业：SHF：黑猩猩：实时节能运动规划的算法-硬件-自动化协同设计探索

• 批准号: 2239945
• 负责人: Bo Yuan
• 金额: $ 50万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239945&HistoricalAwards=false
• 关键词: CAREER; SHF; Chimp; Algorithm; Hardware

As the fundamental and critical robotic task for planning and deciding the actions of robots, motion planning is widely desired in many real-world applications, such as autonomous driving, in-warehouse package handling, assisted surgery etc. To date, there exists an increasing performance gap between the intensive computation of modern motion planning workloads and the insufficient support from general-purpose hardware, calling for efficient hardware acceleration to realize real-time energy-efficient high-quality planning. This project proposes Chimp, a cross-layer co-design framework for highly efficient motion planning processor. Chimp aims to develop a new design paradigm that can efficiently integrate domain expertise into learning-based motion planning, improving the planning reliability and performance. This project will significantly promote the intelligence and durability of modern autonomous systems, enhancing the economic opportunities in many fields such as autonomous driving, smart manufacturing, and intelligent healthcare. This project will enrich the curriculum of the university and promote the involvement of students from underrepresented minority groups, undergraduates and K-12 students in the STEM fields.This project aims to perform algorithm-hardware-automation co-exploration to simultaneously enable high planning performance and high hardware performance. It delivers innovations at three levels: (1) it develops key design principles that can guide the efficient integration of domain expertise to the construction of high-performance learning-based motion planners in complex physical-world settings and resource-constrained scenarios; (2) it builds new hardware primitives that specifically support the unique computing patterns in motion planning. It also proposes a series of optimization techniques for dataflow and microarchitecture, improving hardware efficiency and system utilization; and (3) it offers automatic design, mapping and evaluation of the motion planning model and hardware with different algorithmic, architectural and application constraints and budgets, enabling the improved efficiency of design flow and better exploration of design space. Both software and hardware implementation and evaluation will be performed on robotic simulators, Field-programmable gate array boards and real-world robots in different working environments. The research outcomes of this project will advance various technical fields, such as computing hardware, robotics and machine learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

作为规划和决定机器人动作的基础和关键机器人任务，运动规划在许多现实应用中被广泛期望，例如自动驾驶、仓库内包裹处理、辅助手术等。迄今为止，现代运动规划工作负载的密集计算与通用硬件的支持不足之间存在着越来越大的性能差距，需要高效的硬件加速，实现实时节能高质量规划。该项目提出了Chimp，一个高效运动规划处理器的跨层协同设计框架。Chimp旨在开发一种新的设计范式，可以有效地将领域专业知识集成到基于学习的运动规划中，提高规划的可靠性和性能。该项目将显著促进现代自动驾驶系统的智能化和耐用性，增加自动驾驶、智能制造和智能医疗等多个领域的经济机会。该项目将丰富大学的课程，并促进来自代表性不足的少数群体的学生，本科生和K-12学生参与STEM领域。该项目旨在进行算法-硬件-自动化的共同探索，同时实现高规划性能和高硬件性能。它在三个层面上提供创新：（1）它开发了关键的设计原则，可以指导在复杂的物理世界设置和资源受限的场景中有效整合领域专业知识，以构建基于学习的高性能运动规划器;（2）它构建了专门支持运动规划中独特计算模式的新硬件原语。提出了一系列的优化技术，提高了硬件效率和系统利用率;（3）在不同的算法、架构和应用约束和预算条件下，实现了运动规划模型和硬件的自动设计、映射和评估，提高了设计流程的效率，更好地探索了设计空间。软件和硬件的实施和评估将在机器人模拟器、现场可编程门阵列板和不同工作环境中的真实机器人上进行。该项目的研究成果将推动计算硬件、机器人和机器学习等各个技术领域的发展。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

DynGMP: Graph Neural Network-Based Motion Planning in Unpredictable Dynamic Environments

• DOI: 10.1109/iros55552.2023.10342326
• 发表时间: 2023-10
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Wenjin Zhang;Xiao Zang;Lingyi Huang;Yang Sui;Jingjin Yu;Yingying Chen;Bo Yuan

GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search

GraphMP：具有高效图搜索的基于图神经网络的运动规划

• 发表时间: 2023
• 期刊: NeurIPS
• 作者: Zang, X;Yin, M;Xiao, J;Zonouz S;Yuan, B.
• 通讯作者: Yuan, B.