<title>Value-driven behavior generation for an autonomous mobile ground robot</title>

摘要

In this paper, we will describe a value-driven graph search technique that is capable of generating a rich variety of single and multiple vehicle behaviors. The generation of behaviors depends on cost and benefit computations that may involve terrain characteristics, line of sight to enemy positions, and cost, benefit, and risk of traveling on roads. Depending on mission priorities and cost values, real-time planners can autonomously build appropriate behaviors on the fly that include road following, cross-country movement, stealthily movement, formation keeping, and bounding overwatch. This system follows NIST's 4D/RCS architecture, and a discussion of the world model, value judgment, and behavior generation components is provided. In addition, techniques for collapsing a multidimensional model space into a cost space and planning graph constraints are discussed. The work described in this paper has been performed under the Army Research Laboratory's Robotics Demo III program.