Integration of Machine Learning and Sensor-Based Control in Intelligent Robotic Systems

智能机器人系统中机器学习和基于传感器的控制的集成

• 批准号: 9216428
• 负责人: Mark Spong
• 金额: $ 20万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-04-01 至 1996-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9216428&HistoricalAwards=false
• 关键词: Integration; Machine; Learning; Sensor; Based

This award, in the NSF/EPRI Initiative on Intelligent Control, supports research on the integration of machine learning and sensor-based control in intelligent robotic systems. The research is interdisciplinary in nature and combines techniques of explanation-based control with robust and adaptive nonlinear control, computer vision, and motion planning. The goal is to go beyond the strict hierarchical control architectures typically found in robotic systems by integrating modeling, dynamics, and control at all levels of intelligence. Ultimately, techniques of nonlinear dynamics and control will be combined with artificial intelligence into a single new paradigm in which symbolic reasoning holds an equal place with differential equation based modeling and control. Both theoretical and experimental investigations will be conducted.

该奖项属于 NSF/EPRI 智能控制计划，支持智能机器人系统中机器学习和基于传感器的控制集成的研究。 该研究本质上是跨学科的，并将基于解释的控制技术与鲁棒自适应非线性控制、计算机视觉和运动规划相结合。 目标是通过集成各个智能级别的建模、动力学和控制，超越机器人系统中通常存在的严格分层控制架构。 最终，非线性动力学和控制技术将与人工智能结合成一个新的范式，其中符号推理与基于微分方程的建模和控制具有同等的地位。将进行理论和实验研究。