Deployment and Optimization of Wireless ad-hoc Communication Networks

From DRLWiki

(Difference between revisions)
Jump to: navigation, search
(Collaborators)
 
(7 intermediate revisions not shown)
Line 3: Line 3:
== Approach ==
== Approach ==
-
We are currently developing a robust, mobile communication backbone, which can provide networking capability to other robotic agents, sensor networks, or people using a team of 10 iRobot Create robots, Atheros embedded Linux 802.11g radio, accelerometer and GPS. The system can be programmed using the swarm programming language MIT Proto. We are particular interested by a low-cost solution, which achieves robust behavior using only scarce (or none) positional information.
+
We have developed a platform for autonomous deployment of a mobile communication backbone, which can provide networking capability to other robotic agents, sensor networks, or people. We are experimenting with a team of 10 iRobot Create robots, Atheros embedded Linux 802.11g radio, accelerometer and GPS. The system can be programmed using the swarm programming language MIT Proto.  
 +
 
 +
Our algorithms and hardware design are particularly motivated by a low-cost solution, which achieves robust behavior using only scarce (or none) positional information. Building up on a fully reactive algorithm that requires no positional information, we incrementally develop algorithms with better performance that rely on more and more accurate sensor information.
== Status ==
== Status ==
-
So far, we developed a fully distributed, reactive algorithm for deployment and maintenance of a mesh network. The algorithm has minimalist requirements on the individual robotic node, which are limited to wireless signal strength estimation and bumper sensors. This makes the proposed solution suitable for deployment of large numbers of comparably cheap mobile communication nodes. Robots explore the configuration space by random walk and stop only if their current location satisfies user-specified constraints on connectivity and network topology. Resulting deployments are robust and convergence is analyzed using both kinematic simulation with a simplified collision and communication model as well as a probabilistic macroscopic model.
+
We developed a fully distributed, reactive algorithm for deployment and maintenance of a mesh network. The algorithm has minimalist requirements on the individual robotic node, which are limited to wireless signal strength estimation and bumper sensors. This makes the proposed solution suitable for deployment of large numbers of comparably cheap mobile communication nodes. Robots explore the configuration space by random walk and stop only if their current location satisfies user-specified constraints on connectivity and network topology. Resulting deployments are robust and convergence is analyzed using both kinematic simulation with a simplified collision and communication model as well as a probabilistic macroscopic model.
 +
 
 +
Experiments are carried out on the MIT Hockeyfield and in the basement of the Stata Center.
<center>
<center>
{|
{|
|valign="middle"|[[Image:wificoverage.jpg|center|thumb=wificoverage.jpg|300px|Figure 1. A team of iRobot Create on the MIT Hockeyfield.]]
|valign="middle"|[[Image:wificoverage.jpg|center|thumb=wificoverage.jpg|300px|Figure 1. A team of iRobot Create on the MIT Hockeyfield.]]
 +
|valign="middle"|[[Image:statacenter.png|center|thumb|300px|Figure 2. Deployment result after 35min in the basement of the MIT Stata Center. The robot in the center was the initial deployment location. Numbers indicate the numbers of nodes each robot can see in the network. Coverage is approximately 500m2.]]
|}
|}
</center>
</center>
Line 16: Line 21:
== People ==
== People ==
Nikolaus Correll
Nikolaus Correll
 +
Daniela Rus
== Collaborators ==
== Collaborators ==
-
Jonathan Bachrach, Makani Power
+
Jonathan Bachrach, Makani Power, [http://hiopa16.com Anna Derbakova]
 +
 
 +
== Publications ==
 +
N. Correll, D. Rus, J. Bachrach and D. Vickery. Ad-hoc Wireless Network Coverage with Networked Robots that Cannot Localize. In IEEE International Conference on Robotics and Automation, Kobe, Japan, to appear May 2009.

Latest revision as of 15:00, 28 January 2011

Contents for This Page

Motivation

Wireless communication is a key technology in Distributed Intelligent Systems. Robotics applications pose challenges to current technology due to increased mobility and a large variety of throughput and latency requirements.

Approach

We have developed a platform for autonomous deployment of a mobile communication backbone, which can provide networking capability to other robotic agents, sensor networks, or people. We are experimenting with a team of 10 iRobot Create robots, Atheros embedded Linux 802.11g radio, accelerometer and GPS. The system can be programmed using the swarm programming language MIT Proto.

Our algorithms and hardware design are particularly motivated by a low-cost solution, which achieves robust behavior using only scarce (or none) positional information. Building up on a fully reactive algorithm that requires no positional information, we incrementally develop algorithms with better performance that rely on more and more accurate sensor information.

Status

We developed a fully distributed, reactive algorithm for deployment and maintenance of a mesh network. The algorithm has minimalist requirements on the individual robotic node, which are limited to wireless signal strength estimation and bumper sensors. This makes the proposed solution suitable for deployment of large numbers of comparably cheap mobile communication nodes. Robots explore the configuration space by random walk and stop only if their current location satisfies user-specified constraints on connectivity and network topology. Resulting deployments are robust and convergence is analyzed using both kinematic simulation with a simplified collision and communication model as well as a probabilistic macroscopic model.

Experiments are carried out on the MIT Hockeyfield and in the basement of the Stata Center.

Figure 1. A team of iRobot Create on the MIT Hockeyfield.
Figure 2. Deployment result after 35min in the basement of the MIT Stata Center. The robot in the center was the initial deployment location. Numbers indicate the numbers of nodes each robot can see in the network. Coverage is approximately 500m2.

People

Nikolaus Correll Daniela Rus

Collaborators

Jonathan Bachrach, Makani Power, Anna Derbakova

Publications

N. Correll, D. Rus, J. Bachrach and D. Vickery. Ad-hoc Wireless Network Coverage with Networked Robots that Cannot Localize. In IEEE International Conference on Robotics and Automation, Kobe, Japan, to appear May 2009.

Personal tools