An Efficient Strategy for Fast Object Search Considering the Robot's Perceptual Limitations

Searching for an object in an environment using a mobile robot is a challenging task that requires an algorithm to define a set of points in which to sense the environment and an effective traversing strategy, to decide the order in which to visit such points. Previous work on sensing strategies normally assume unrealistic conditions like infinite visibility of the sensors. This paper introduces the concept of recognition area that considers the robot’s perceptual limitations. Three new sensing algorithms using the recognition area are proposed and tested over 20 different maps of increasing difficulty and their advantages over traditional algorithms are demonstrated. For the traversing strategy, a new heuristic is defined that significantly reduces the branching factor of a modified Branch & Bound algorithm, producing paths which are not too far away from the optimal paths but with several orders of magnitude faster that a traditional Branch & Bound algorithm.