Distributed Coverage Control
Distributed Control Algorithms for Networked Mobile Robots with
Sensory Feedback
Introduction
We wish to develop distributed algorithms for networked teams of
robots that self-organize in response to the sensed environment. Such
networks promise the ability to collect information over distributed,
large-scale domains with minimum infrastructure maintenance. This
technology will enable scientific studies on geological and ecological
scales previously prohibited by practical considerations, and provide
tools for a host of security and surveillance applications.
Thus far we have focused on the task of controlling the robots so
that their configuration optimizes the sampling of a sensory function.
We consider a group of robots that is dispatched over a bounded space
of interest. The group's task is to sample a sensory function over the
space. The sensory function is an unknown continuous scalar field that
can be measured locally by the robots, such as light intensity,
temperature, sound intensity, or chemical concentration. We have
developed a decentralized control solution that can accomplish this
task. Using sensory measurements and neighbor positions, the network
self-organizes by positioning individual robots to optimize the
measurement of the sensory function. This enables the network to
record observations about the sensory environment with varying
resolution, so that areas with larger sensory signals receive
higher-density data observations than areas that are quiet. Our work
builds on several important results in this area, notably [1].
New Algorithms
We have developed a control algorithm by which robots are caused to
move to the weighted centroid of their Voronoi regions, a position
that has been shown to be optimal for sensing [2]. For each robot, the
control algorithm is as follows:
1. Compute the local Voronoi region.
2. Measure the value and gradient of the sensory function and
compute a linear estimate of the sensory function using these
measurements.
3. Compute the centroid of the Voronoi region using the estimated
sensory function as a density.
4. Move in the direction of the centroid.
5. Repeat.
In addition, techniques have been developed for the efficient
computation of the above algorithm for implementation on platforms
with limited computational resources. Stability and robustness of this
algorithm is being investigated and the development of other
algorithms is ongoing.
Simulation Results
The algorithm has been tested in numerical simulation. An example
of the final state of a typical simulation run is shown above. The
red "x" denotes the center of a Gaussian sensory function, the circles
represent robot positions, and the polygons show the Voronoi regions
of the robots.
Simulation Results
The algorithm has also been implemented and tested on a swarm of
actual mobile robots. An example of the final state of a typical
experiment is shown above. The robots use light sensors to measure
the light source at the right of the region.
References:
[1] J. Cortes, S. Martinez & F. Bullo. Coverage Control for
Mobile Sensing Networks. IEEE Transactions on Robotics and Automation,
Vol. 20, No. 2, pp.243-255, April 2004.
[2] Mac Schwager, James McLurkin & Daniela Rus. Distributed
Coverage Control with Sensory Feedback for Networked Robots. Submitted
to Robotic: Science and Systems, Philadelphia, PA, Aug. 16-19,
2006.
