Information-driven Planning and Control for Active and Mobile Sensing
Professor of Engineering and Computer Science
Department of Mechanical Engineering and Materials Science
Laboratory for Intelligent Systems and Controls (LISC)
Unmanned ground, aerial, and underwater vehicles equipped with on-board wireless sensors are becoming crucial to both civilian and military applications because of their ability to replace or assist humans in carrying out dangerous yet vital missions. As they are often required to operate in unstructured and uncertain environments, these mobile sensor networks must be adaptive and econfigurable, and decide future actions intelligently based on the sensor measurements and environmental information. Recent work on geometric and information-driven sensor path planning has shown that the performance of these sensors can be significantly improved by planning their paths based on probabilistic sensor models, and on the geometric characteristics of the workspace and of the sensor field-of-view or visibility region. This talk discusses a general framework by which the expected information value of sensor measurements can be described by information theoretic functions in closed form, and, consequently, used to derive path planning and control laws for active sensing and information gathering.