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CPS: Medium: LEAR-CPS: Low-Energy computing for Autonomous mobile Robotic CPS via Co-Design of Algorithms and Integrated Circuits

CPS：中：LEAR-CPS：通过算法和集成电路的协同设计实现自主移动机器人 CPS 的低能耗计算

基本信息

批准号：
1837212
负责人：
Sertac Karaman
金额：
$ 100万
依托单位：
Massachusetts Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837212&HistoricalAwards=false
关键词：
CPS Medium LEAR Low Energy

项目摘要

The goal of this research is to enable a new era of low-energy mobile robotic Cyber-Physical Systems (CPS). The approach is the simultaneous design of the computing hardware with the computer algorithms, with input from the physics of the system. Applications include, but are not limited to, insect-size robotic bees for artificial pollination, robotic water striders for environmental monitoring, miniature underwater autonomous vehicles for inspection, orally-administered medical robotic vehicles that can intelligently navigate the digestive system, robotic gliders that can operate in the air or underwater for months at a time, and many more. The results will enable low-power computing for artificial intelligence and autonomy to complement the existing low-energy, miniature actuation and sensing systems that have already been developed. This will enable low-energy, miniature mobile robotic CPSs that can still provide provable guarantees on completeness, optimality, robustness and safety. This project will focus on the development of novel algorithms and novel computing hardware for miniature, energy-efficient mobile robotic CPS. The proposed research will enable low-energy computation for full autonomy by way of minimizing energy consumption during design time and run time, by simultaneously designing the algorithms and the computing hardware. Decision making algorithms will minimize computing energy during run time, for instance, by considering motions that may not require heavy computation for perception and planning. The project will demonstrate the new methods by constructing the smallest fully-autonomous aerial robotic vehicle ever built. We believe the proposed foundational research and the proposed demonstration will kickstart a new cyber-physical systems subfield at the intersection of the mobile robotics literature and the computing hardware (circuits) literature.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.

这项研究的目标是实现一个低能量移动机器人网络物理系统(CPS)的新时代。这种方法是同时设计计算硬件和计算机算法，输入来自系统的物理。应用包括但不限于用于人工授粉的昆虫大小的机器蜜蜂，用于环境监测的机器人水行者，用于检查的微型水下自主车辆，可以智能导航消化系统的口服医疗机器人车辆，可以在空中或水下一次操作数月的机器人滑翔机，等等。这一成果将使人工智能和自主的低功耗计算能够补充已经开发的现有的低能耗、微型驱动和传感系统。这将使低能耗、微型移动机器人CPSS仍然能够在完整性、最佳性、健壮性和安全性方面提供可证明的保证。该项目将专注于为微型、节能的移动机器人CPS开发新的算法和新的计算硬件。该研究将通过同时设计算法和计算硬件来最小化设计时和运行时的能量消耗，从而实现完全自主的低能量计算。决策算法将在运行时最大限度地减少计算能量，例如，通过考虑可能不需要大量计算来感知和规划的运动。该项目将通过建造有史以来最小的全自动空中机器人来演示新方法。我们相信，拟议的基础研究和拟议的演示将在移动机器人文献和计算硬件(电路)文献的交叉点启动一个新的网络物理系统子领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Efficient Computation of Map-scale Continuous Mutual Information on Chip in Real Time

芯片上地图尺度连续互信息的实时高效计算

DOI：
10.1109/iros51168.2021.9636603
发表时间：
2021
期刊：
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
影响因子：
0
作者：
Gupta, Keshav;Li, Peter Zhi;Karaman, Sertac;Sze, Vivienne
通讯作者：
Sze, Vivienne

Memory-Efficient Gaussian Fitting for Depth Images in Real Time

实时深度图像的内存高效高斯拟合