Sensor planning for 3D object search

In this paper, we provide a systematic study of the task of sensor planning for object search. The search agent's knowledge of object location is enco ded as a discrete probability density which is updated whenever a sensing a ction occurs, Each sensing action of the agent is defined by a viewpoint, a viewing direction, a field-of-view, and the application of a recognition a lgorithm. The formulation casts sensor planning as an optimization problem: the goal is to maximize the probability of detecting the target with minim um cost. This problem is proved to be NP-Complete, thus a heuristic strateg y is favored. To port the theoretical framework to a real working system, w e propose a sensor planning strategy for a robot equipped with a camera tha t can pan, tilt, and zoom. In order to efficiently determine the sensing ac tions over time, the huge space of possible actions with fixed camera posit ion is decomposed into a finite set of actions that must be considered. The next action is then selected from among these by comparing the likelihood of detection and the cost of each action, When detection is unlikely at the current position, the robot is moved to another position for which the pro bability of target detection is the highest. (C) 1999 Academic Press.