RI: Medium: To Sense or Not to Sense: Energy Efficient Adaptive Sensing for Autonomous Systems

RI：中：感知或不感知：自主系统的节能自适应传感

基本信息

批准号：
1900821
负责人：
Srinivasa Narasimhan
金额：
$ 120万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1900821&HistoricalAwards=false
关键词：
RI Medium Sense Energy Efficient

项目摘要

Sensing and computation have been crucial to the significant progress in semi- and fully-autonomous vehicles and robots. Proliferation of many types of sensors (LIDARs, cameras, RADARs, etc.) and the advent of compute-heavy and data-hungry deep-learning approaches have increased the performance of autonomous systems by leaps and bounds. But the wide variety of sensors differ in terms of their performance, cost, and operational difficulty. Thus, specific sets of sensors are chosen for a particular task on a particular robot. This horses-for-courses approach often results in one-off systems that are incapable of adapting to many tasks or robots. Thus, to ensure safety and reliability, multi-tasking systems like autonomous vehicles have resorted to over-engineering, with upwards of 15 sensors and multiple GPUs/CPUs in any car. And, to make matters worse, many of the sensed data is eventually discarded as unwanted background. Thus, while the energy footprint of sensing and computations is increasing at an alarming rate, the flexibility or adaptability of these systems is still lacking. Much of this state of affairs can be attributed to the fact that sensors and algorithms face vastly different hardware and software challenges and are hence designed, developed, and manufactured in separate academic units or industries. This project takes a different approach: adaptively sense mostly (if not only) quantities which help solve the task accurately and within the allotted time. In other words, this project advocates folding adaptive and flexible sensing within a learning framework for autonomous systems. This is achieved by co-design and co-execution of sensing and algorithms to maximize accuracy and flexibility while minimizing expended energy and cost. The approach is motivated by how humans decide what, where, when, and how to sense and apply that to a robot learning framework. Research and education are closely integrated in a diverse and inclusive environment.The project consists of three fundamental research thrusts. Thrust 1: Development of highly novel and fully adaptive design and physical realization of 3D optical sensors. This thrust includes a fundamental mathematical framework that determines the optimal set of emitted and measured rays to achieve a particular task at hand. This is the mathematical foundation for developing a new class of sensors that detect and characterize obstacles---a time critical task of any autonomous system---with maximum energy efficiency, minimal latency (i.e., near-instantly) and with virtually no separate computation. Thrust 2: Novel decision-making framework that efficiently controls the adaptive sensors for the task at hand. This includes determining where and when to sense and adapting behavior policies accordingly. Thrust 3: Support the robot learning framework by learning and interacting with humans. The project will demonstrate the generality of adaptive sensing using three disparate autonomous systems that have broad societal impact: a) autonomous vehicles, b) assistive robots, and c) robots in manufacturing.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.

传感和计算对于半自动和全自动车辆和机器人的重大进展至关重要。多种类型传感器（激光雷达、摄像头、雷达等）的激增以及计算量大、数据量大的深度学习方法的出现，使得自主系统的性能突飞猛进。但传感器种类繁多，其性能、成本和操作难度各不相同。因此，为特定机器人上的特定任务选择特定的传感器组。这种以马换路的方法通常会导致一次性系统无法适应许多任务或机器人。因此，为了确保安全性和可靠性，自动驾驶汽车等多任务系统已经诉诸过度设计，每辆车都配备了多达 15 个传感器和多个 GPU/CPU。而且，更糟糕的是，许多感测到的数据最终会作为不需要的背景而被丢弃。因此，虽然传感和计算的能量足迹正在以惊人的速度增加，但这些系统仍然缺乏灵活性或适应性。这种状况很大程度上可以归因于这样一个事实：传感器和算法面临着截然不同的硬件和软件挑战，因此是在不同的学术单位或行业中设计、开发和制造的。该项目采用了不同的方法：自适应地感知大部分（如果不是唯一）有助于在指定时间内准确解决任务的数量。换句话说，该项目提倡在自主系统的学习框架内折叠自适应和灵活的传感。这是通过传感和算法的共同设计和共同执行来实现的，以最大限度地提高准确性和灵活性，同时最大限度地减少消耗的能源和成本。该方法的动机是人类如何决定什么、在哪里、何时以及如何感知并将其应用到机器人学习框架中。研究和教育在多元化和包容性的环境中紧密结合。该项目由三个基本研究方向组成。主旨 1：开发高度新颖且完全自适应的 3D 光学传感器设计和物理实现。该推力包括一个基本的数学框架，该框架确定最佳的发射和测量射线集以实现手头的特定任务。这是开发新型传感器的数学基础，该传感器可检测和表征障碍物（任何自主系统的一项时间关键任务），具有最高的能源效率、最小的延迟（即接近即时）并且几乎不需要单独的传感器计算。主旨 2：新颖的决策框架，可有效控制自适应传感器来完成手头的任务。这包括确定何时何地感知并相应地调整行为策略。主旨 3：通过与人类学习和交互来支持机器人学习框架。该项目将使用三种具有广泛社会影响的不同自主系统来展示自适应传感的普遍性：a) 自动驾驶车辆，b) 辅助机器人和 c) 制造机器人。该奖项反映了 NSF 的法定使命，并被认为值得支持通过使用基金会的智力优点和更广泛的影响审查标准进行评估。