Active PedestrianFollowing Using Laser RangeFinder
I. INTRODUCTION
The ability of robots to track and follow moving targetsis essentialto many real life applications such as museum guidance, office or library assistance.On top of being able to track the pedestrians,one aspectof human-robot interaction is robot 's ability to follow a pedestrian target in an indoor environment. There are various scenarioswhere the robot can be given instructions such as holding books in a library or carrying groceriesat a storewhile following the pedestriantarget.
The key componentsof moving target following technique include Simultaneous Localization and Mapping(SLAM), Detecting and Tracking Moving Objects (DATMO),and motion planning. More often than not, the robots are required to operate in dynamic environments where there are multiple pedestriansand obstacles in the surroundings.Consequently,tracking and following a specific target pedestrian become much more challenging. In other words, the following behaviors must be robust enoughto deal with constantocclusionsandobstacleavoidances.
When designing the following algorithm, one intuitive approach is to set the target location as the destination for the robot. However, this approachcan easily lead to losing the target becauseit doesnot react to the target 'mo s tion nor consider the visibility problem (since the target may be occluded by obstacles and become invisible). For achieving robust target following and tracking, the robot should have the intelligent to predict targetmotion and gather observationsactively.
In this paper, we propose a moving target following planner which is able to manage obstacle avoidance and target visibility problems. Experimental results are shown to compare the intuitive approachwith our approachand prove the importance of active information gathering in planning.This paper is organized as follow: Section II discussesrelated works of DATMO and planning algorithms. Section III describes our DATMO system and introduces our target following planner. Lastly, Section IV illustrates the experimental results.
II. RELATED WORKS
There are various approachesto detectand track moving objects suchas building static and dynamic occupancy grid maps proposedby Wolf & Sukhatme[1], finding local minima in the laser scan as in Horiuchi et al. 'w s ork [2] or using machine learning methods in Spinello et al. 'w s ork [3]. Most of DATMO approachesassume that the robot is stationary or has perfect odometry. When tracking moving objects using mobile robots, it has been proven in Wang et al. 'w s ork [4], that SLAM and
DATMO can be done simultaneouslyif the measurementscan be divided into static and
