On the Design and Use of a Micro Air Vehicle to Track and Avoid Adversaries

Authors:
Ruijie He;Abraham Bachrach;Michael Achtelik;Alborz Geramifard;Daniel Gurdan;Samuel Prentice;Jan Stumpf;Nicholas Roy
Affiliations:
Computer Science and Artificial Intelligence Laboratory,Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139,USA;Computer Science and Artificial Intelligence Laboratory,Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139,USA;Ascending Technologies GmbH, Graspergerstrasse 8, 82131Stockdorf, Germany;Computer Science and Artificial Intelligence Laboratory,Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139,USA;Ascending Technologies GmbH, Graspergerstrasse 8, 82131Stockdorf, Germany;Computer Science and Artificial Intelligence Laboratory,Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139,USA;Ascending Technologies GmbH, Graspergerstrasse 8, 82131Stockdorf, Germany;Computer Science and Artificial Intelligence Laboratory,Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139,USA
Venue:
International Journal of Robotics Research
Year:
2010

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Abstract

The MAV â聙聶08 competition focused on the problem of using air and ground vehicles to locate and rescue hostages being held in a remote building. To execute this mission, a number of technical challenges were addressed, including designing the micro air vehicle (MAV), using the MAV to geo-locate ground targets, and planning the motion of ground vehicles to reach the hostage location without detection. In this paper, we describe the complete system designed for the MAV â聙聶08 competition, and present our solutions to three technical challenges that were addressed within this system. First, we summarize the design of our MAV, focusing on the navigation and sensing payload. Second, we describe the vision and state estimation algorithms used to track ground features, including stationary obstacles and moving adversaries, from a sequence of images collected by the MAV. Third, we describe the planning algorithm used to generate motion plans for the ground vehicles to approach the hostage building undetected by adversaries; these adversaries are tracked by the MAV from the air. We examine different variants of a search algorithm and describe their performance under different conditions. Finally, we provide results of our systemâ聙聶s performance during the mission execution.