An Architecture for Person-Following using Active Target Search

This paper addresses a novel architecture for person-following robots using active search. The proposed system can be applied in real-time to general mobile robots for learning features of a human, detecting and tracking, and finally navigating towards that person. To succeed at person-following, perception, planning, and robot behavior need to be integrated properly. Toward this end, an active target searching capability, including prediction and navigation toward vantage locations for finding human targets, is proposed. The proposed capability aims at improving the robustness and efficiency for tracking and following people under dynamic conditions such as crowded environments. A multi-modal sensor information approach including fusing an RGB-D sensor and a laser scanner, is pursued to robustly track and identify human targets. Bayesian filtering for keeping track of human and a regression algorithm to predict the trajectory of people are investigated. In order to make the robot autonomous, the proposed framework relies on a behavior-tree structure. Using Toyota Human Support Robot (HSR), real-time experiments demonstrate that the proposed architecture can generate fast, efficient person-following behaviors.Comment: 7 pages, 6 figure

EXTRACTING TOPOLOGICAL INFORMATION FROM GRID MAPS FOR ROBOT NAVIGATION

In many robotic navigation-related tasks, abstracting the environment where mobile robots carry out some mission can be of a great benefit. In particular, extracting a simple topological graph-like representation from a more complex and detailed metric map is often required for path-planning and navigation. In this work, an approach to perform such extraction in grid maps is presented. The focus is not only on obtaining a diagram or visual representation of possible paths, but also to propose a new way to obtain graph data information related to the connectivity of the environment that can be passed to robots or to a centralized planner, in order to assist the navigation task. The approach proposed is based on image processing techniques. Simulation results prove its simplicity, accuracy and efficiency.