Networked Quad-Rotor Flying Robots in Multi-Agent Systems

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We wish to develop distributed algorithms for networked quad-rotor flying robots in multi-agent systems. Such robot teams are useful in a broad range of surveillance, security, and telecommunication applications. We explore high-dimensional (3+) coverage controllers, network update schemes, and vision-based localization methods.

Contents for This Page

Approach

Our quad-rotor flying robot fleet is composed of six AscTec Hummingbirds. Pitch, roll, and yaw are fully stabilized by the onboard controller described in [1]. Three of the robots are also equipped with an AscTec AutoPilot module, which allows for GPS and altitude position control. We developed an onboard ARM microprocessor module that acquires state estimations from the robot and wireless communicates them to other robots using an xBee module. The same ARM modules run our control algorithms to self-organize in both indoor (motion capture system) and outdoor (GPS, vision localization) environments. Our goal is to realize our researched algorithms on this hardware in fully distributed fashion.

Figure 1. A robot autonomously flying indoors using a motion capture system (photo courtesy of Jason Dorfman, CSAIL Photographer)
Figure 1. A robot autonomously flying indoors using a motion capture system (photo courtesy of Jason Dorfman, CSAIL Photographer)

Optimal Camera Placement

We developed a coverage algorithm for hovering agents that move in three dimension, but monitor a two dimensional environment. In [2] we focused on the specific scenario of flying or floating robots with downward-facing cameras. We address the question of how to deploy multiple robots so that together their cameras produce the most informative image of the environment. Surveillance and environmental monitoring applications are numerous for such an algorithm. We propose a cost function describing the aggregate information per pixel for the group of robots. Taking the gradient with respect to each robot's position yields a distributed controller for driving the robots to locally optimal positions. Figure 2 shows numerical simulations and robots experiments, respectively, for the algorithm. Refer to the Distributed Control Algorithms for Networked Mobile Robots page for more work in this area.

Figure 2. Movie showing experiments and simulations of our camera coverage algorithm.
Figure 2. Movie showing experiments and simulations of our camera coverage algorithm.

People

Brian J. Julian

Mac Schwager

Daniela Rus

Collaborators

Michael Angermann, Institute of Communications and Navigation, German Aerospace Center (DLR)

References

[1] D. Gurdan, J. Stumpf, M. Achtelik, K.M. Doth, G. Hirzinger, D. Rus - Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1kHz

The 2006 International Conference on Robotics and Automation. , September, 2006
Bibtex

[2] M. Schwager, B. J. Julian, D. Rus - Optimal Coverage for Multiple Hovering Robots with Downward-Facing Cameras

In the Proceedings of the International Conference on Robotics and Automation (ICRA 09), Accepted , Kobe, Japan, May, 2009
Bibtex