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.

该 I-Corps 项目更广泛的影响/商业潜力是开发可靠的自动驾驶车辆系统，该系统可以利用传感器测量来在车辆行驶时调整其路径，从而克服现有的限制。在机载机器人平台上使用时，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、更安全的自动驾驶系统，另一个例子是可以帮助收集质量数据的自主空中/水下车辆。野生动物栖息地和定义现有自主系统的要求，同时最大限度地减少对自然环境的破坏，将有助于建立自主机器人系统可靠设计的通用标准。该 I-Corps 项目基于传感器路径规划算法的开发，该算法可以该算法使用信息论和概率传感器测量模型来决定车辆的路径，以便获得信息最丰富的测量结果，同时考虑到置信度。基于物体识别，车辆可以自动根据实时收集的信息重新规划其路径该技术基于对利用和处理传感器测量进行路径规划的有效且高效的方法的研究。路径规划算法的应用将提供更高水平的自主性，从而形成更可靠的集成系统。该奖项是 NSF 的法定使命，并通过使用评估被认为值得支持。基金会的智力价值和更广泛的影响审查标准。