I-Corps: Neuromorphic Target Tracking and Control for Insect-Scale Aerial Vehicles

I-Corps：昆虫级飞行器的神经形态目标跟踪和控制

• 批准号: 1838470
• 负责人: Silvia Ferrari
• 金额: $ 5万
• 依托单位: Cornell University
• 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: 2018-07-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838470&HistoricalAwards=false
• 关键词: Corps; Neuromorphic; Target; Tracking; Control

The broader impact/commercial potential of this I-Corps project is to enable a broader range of end users to utilize the capabilities of autonomous robotics and to make advanced autonomous systems more broadly accessible and reliable. Robotics such as micro aerial vehicles (MAVs), self-driving cars, and other ground-based service robots are playing increasingly important roles in the lives of many industries as advances in autonomy enable them to be used safely and reliably in diverse situations. Accurately tracking targets and navigating while avoiding obstacles are important prerequisites to fully autonomous operation for robots. Neuromorphic cameras can more accurately detect motion than traditional cameras while consuming far less power. This project will explore the commercial applications of algorithms which interpret the data from neuromorphic cameras to enable autonomous systems to accurately track motion and navigate in unknown environments. By enabling more accurate sensing with reduced power consumption, these algorithms will increase the safety and reliability of autonomous systems. The proposed techniques will enable autonomous systems to react safely and robustly in real time to unexpected environmental changes without immediate operator intervention.This I-Corps project will explore the commercialization of neuromorphic sensing and control algorithms that enable accurate environmental sensing from moving robotic platforms. Autonomous navigation requires processing data from exteroceptive sensors for the purposes of obstacle avoidance and target tracking. These tasks must be accomplished in real time with minimal latency to maximize the capabilities and reliability of the autonomous robot. Neuromorphic cameras sense the environment with sub-millisecond latency and, unlike traditional cameras, provide information only about changes in the scene. The algorithms which will be explored by this project efficiently process the data from neuromorphic sensors to detect the presence of moving targets and stationary obstacles to enable efficient autonomous control for high-speed aerial and ground-based robots in uncertain and rapidly changing environments. These algorithms include neuromorphic control techniques which enable autonomous control in the presence of both environmental uncertainties such as disturbances and uncertain variations in the physical parameters of the robot. The proposed techniques have been validated in high-fidelity simulations using benchmark datasets and have been shown to be capable of rapid adaptation to unexpected changes while maintaining control of the autonomous system.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.

这个I-Corps项目的更广泛的影响/商业潜力是使更广泛的最终用户能够利用自主机器人的能力，并使先进的自主系统更广泛地获得和可靠。微型飞行器（MAV）、自动驾驶汽车和其他地面服务机器人等机器人在许多行业的生活中发挥着越来越重要的作用，因为自动化的进步使它们能够在各种情况下安全可靠地使用。准确跟踪目标并在避开障碍物的同时导航是机器人完全自主操作的重要前提。神经形态摄像机可以比传统摄像机更准确地检测运动，同时消耗更少的功率。该项目将探索算法的商业应用，这些算法解释来自神经形态摄像机的数据，使自主系统能够在未知环境中准确跟踪运动和导航。通过降低功耗实现更准确的传感，这些算法将提高自主系统的安全性和可靠性。所提出的技术将使自主系统能够在真实的时间内对意外的环境变化做出安全和鲁棒的反应，而无需操作员立即干预。自主导航需要处理来自外感受传感器的数据，以用于避障和目标跟踪。这些任务必须以最小的延迟在真实的时间内完成，以最大限度地提高自主机器人的能力和可靠性。神经形态摄像机以亚毫秒级的延迟感知环境，与传统摄像机不同，它只提供有关场景变化的信息。该项目将探索的算法有效地处理来自神经形态传感器的数据，以检测移动目标和静止障碍物的存在，从而在不确定和快速变化的环境中对高速空中和地面机器人进行有效的自主控制。这些算法包括神经形态控制技术，这些技术能够在存在环境不确定性（例如干扰）和机器人物理参数不确定变化的情况下实现自主控制。所提出的技术已在使用基准数据集的高保真模拟中得到验证，并已被证明能够快速适应意外变化，同时保持对自主系统的控制。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。