I-Corps: Real-time intelligent sensor path planning based on information value estimation

I-Corps：基于信息价值估计的实时智能传感器路径规划

• 批准号: 2038358
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038358&HistoricalAwards=false
• 关键词: Corps; Real; time; intelligent; sensor

The broader impact/commercial potential of this I-Corps project is the development of reliable autonomous vehicle systems that may overcome existing limitations by utilizing sensor measurements to adapt its path while the vehicle is in motion. As RADAR, LiDAR, and SONAR sensor can be used on the onboard robotic platforms, markets may utilize autonomous aerial, underwater, and ground vehicles. One application is the development of a more reliable and safe autonomous driving system. Another example is an autonomous aerial/underwater vehicle that may assist in gathering quality data of wild animal habitats and populations while minimizing the damage of natural environment. Defining the requirements of the existing autonomous systems will help establish a general standard for a reliable design for autonomous robotic systems.This I-Corps project is based on the development of a sensor path planning algorithm that may adapt its direction based on sensor measurements while the vehicle is in motion. Information theory and probabilistic sensor measurement models are used in the algorithm to decide the vehicle’s path such that the most informative measurements can be obtained. The algorithm is capable of considering the confidence level based on object identification and the vehicle can automatically re-plan its path based on the information gathered in real-time. This technology is based on research on effective and efficient methods to utilize and process the sensor measurements towards path planning. Technical results demonstrate that the proposed algorithm can compute the minimum-time path while achieving a satisfactory confidence level from obtained sensor measurements. The application of the path planning algorithm will give a higher level of autonomy, resulting in a more reliable integrated 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/商业潜力是开发可靠的自主车辆系统，该系统可以通过利用传感器测量来克服现有的限制，从而在车辆运动时调整其路径。 由于雷达、激光雷达和声纳传感器可以用在机载机器人平台上，市场可能会利用自主空中、水下和地面车辆。 一个应用是开发更可靠和安全的自动驾驶系统。另一个例子是自主航空/水下航行器，它可以帮助收集野生动物栖息地和种群的高质量数据，同时最大限度地减少对自然环境的破坏。定义现有的自主系统的要求将有助于建立一个通用的标准，为自主机器人系统的可靠设计，这个I-Corps项目是基于传感器路径规划算法的发展，可以适应其方向的基础上传感器测量，而车辆在运动中。该算法采用信息论和概率传感器测量模型来决定车辆的路径，从而获得最具信息量的测量值。该算法能够考虑基于对象识别的置信水平，并且车辆可以基于实时收集的信息自动重新规划其路径。该技术基于对有效和高效的方法的研究，以利用和处理用于路径规划的传感器测量。技术结果表明，所提出的算法可以计算最短时间的路径，同时达到满意的置信水平从获得的传感器测量。路径规划算法的应用将赋予更高的自主性，从而形成更可靠的集成系统。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。