Title :
Task-directed computation of qualitative decisions from sensor data
Author :
Hager, Gregory D.
Author_Institution :
Dept. of Comput. Sci., Yale Univ., New Haven, CT, USA
fDate :
8/1/1994 12:00:00 AM
Abstract :
Describes a novel approach to sensor-based decision making based on formulating and solving large systems of parametric constraints. The constraints describe both a model for sensor data and the criteria for correct decisions about the data. An incremental constraint solving technique that performs decision-directed model recovery is developed. This method is straightforward to apply, is easily parallelized, and convergence can be demonstrated under very reasonable structural and statistical assumptions. This approach is demonstrated on several different decision-making problems involving manipulation and categorization of objects observed with a range scanner. The experiments indicate that simultaneous solution of both model constraints and decision criteria can lead to efficient and effective decision making, even when the observed data does not strongly determine a data model
Keywords :
convergence; decision theory; robots; sensor fusion; categorization; decision-directed model recovery; incremental constraint solving technique; parametric constraints; qualitative decisions; range scanner; sensor-based decision making; statistical assumptions; task-directed computation; Data models; Decision making; Deformable models; Mobile robots; Parametric statistics; Robot sensing systems; Sensor phenomena and characterization; Sensor systems; Surface fitting; Surface reconstruction;
Journal_Title :
Robotics and Automation, IEEE Transactions on