<title>Information assimilation research at the University of Michigan for the ARPA unmanned ground vehicle project</title>

摘要

The goal of ARPA's Unmanned Ground Vehicle project is to demonstrate the use of small teams of cooperating autonomous robots (2 - 4 vehicles) to carry out military tasks in an outdoor environment. The role of the University of Michigan within the project focuses on aspects of mission planning, assimilation of information provided by multiple agents, and the interaction between planning and perception. The two aspects of this work related to sensor fusion are planning observation points to maximally reduce hypothesis uncertainty, and information sharing in multivehicle scenarios to reduce the amount of perception required. Observation point planning combines the system's current knowledge about an object with the uncertainty model used to characterize observations for data fusion in order to select optimal points for additional observations. Information sharing selects those detected features in the environment which are predicted to be most useful to other cooperating vehicles in the future, adding them to the multiagent system's model of the environment while ignoring less useful features.