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@MastersThesis{meytal-msc,
author = {Meytal Traub},
title = {Topics in Multi-Robot Teamwork},
school = {{B}ar {I}lan {U}niversity},
year = {2011},
OPTkey = {},
OPTtype = {},
OPTaddress = {},
OPTmonth = {},
OPTnote = {Available at \url{http://www.cs.biu.ac.il/~galk/Publications/b2hd-meytal-msc.html}},
OPTannote = {},
  wwwnote = {}, 
  abstract = {In recent years there is a growing interest in multi-robots systems, where a 
group of $N$ robots are working collaboratively in order to execute a given task. 
This thesis addresses two open challenges in multi-robot systems. The first is 
the challenge of deciding on which robot, out of a group of robots, should 
travel to a goal location, to carry out a task there. The second is the 
challenge of integrating multiple and different multi-robot controllers into a 
robust system.
A common decision problem in multi-robot applications involves deciding on which 
robot, out of a group of $N$ robots, should travel to a goal location, to carry 
out a task there. Trivially,  this decision problem can be solved greedily, by 
selecting the robot with the shortest expected travel time. However, this 
ignores the inherent uncertainty in path traversal times; we may prefer a robot 
that is slower (but always takes the same time), over a robot that is expected 
to reach the goal faster, but on occasion takes a very long time to arrive. In 
the first part of this thesis we make several contributions that address this 
challenge. First, we bring to bear economic decision-making theory, to 
distinguish between different selection policies, based on risk (risk averse, 
risk seeking, etc.). Second, we introduce \emph{social regret} (the difference 
between the actual travel time by the selected robot, and the hypothetical time 
of other robots) to augment decision-making in practice. Then, we carry out 
experiments in simulation and with real robots, to demonstrate the usefulness of  
the selection procedures under real-world settings, and find that travel-time 
distributions have repeating characteristics. 
In the second part, we address the challenge of integrating multiple and 
different multi-robot controllers into a robust system. Multi-robot formations 
are of increasing interest to robotics researchers (as a canonical research 
problem), and to robot system builders (e.g.,for unmanned convoys). Indeed, 
there exists vast literature on various techniques for maintaining formations in 
a variety of settings, and for a variety of robots. However, little attention 
has been given to the possibility of using multiple formation controllers, all 
integrated together for greater formation robustness. In this part, we make two 
contributions. First, we address a key challenge in integration, that of joint 
distributed selection and execution of the correct controller, at the same time. 
We demonstrate how to utilize a teamwork software engine to automate this joint 
selection. Second, we describe one such integrated system, which uses several 
different formation-maintenance controllers for greater robustness. },
}