Human Interaction with Networked Robots
Intent mismatch in human swarm interaction
Swarm robotic systems consist of a large collection of simple robots
with limited sensing, communication, actuation, and computational
capabilities. Robots in a swarm robotic system act according to simple
local rules and exhibit a wide range of behaviors without any
centralized controller. However, because of their inherent simplicity
it is difficult to deploy them for complex missions. To use swarm
robotic systems in a complex mission, presence of human operators are
required to guide the behaviors of the swarm towards accomplishing
mission goals. Two key challenges in human swarm interaction is that (a) the state
information of the robot available to the human may not be accurate
and (b) there may be a mismatch between the intent of the operator and
the robots understanding of the human intent. The error in the swarm
state available to the human and the intent mismatch can happen due to
bandwidth limitations and/or latency in communication and localization error of
individual robots. The research goal here is to understand the effect of these
errors on the human-swarm system performance and investigate methods to mitigate
the effects of the errors.
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S. Nunnally, P. Walker, A. Kolling, N. Chakraborty, M. Lewis, K. Sycara, and M. Goodrich, `` Human Influence of
Robotic Swarms with Bandwidth Limits and Localization Errors '', 2012 IEEE
International Conference on Systems, Man, Cybernetics (IEEE SMC 2012) , Seoul, Korea, October
2012.
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P. Walker, A. Kolling, N. Chakraborty, S. Nunnally, K. Sycara, and M. Lewis, `` Neglect Benevolence
in Human Control of Swarms in the Presence of Latency '', 2012 IEEE International Conference on
Systems, Man, Cybernetics (IEEE SMC 2012) , Seoul, Korea, October 2012.
On leader selection and influencing swarms
Many cooperative control problems ranging from formation following, to
rendezvous to flocking can be expressed as consensus problems. The
ability of an operator to influence the development of consensus
within a swarm therefore provides a basic test of the quality of
human-swarm interaction (HSI). Two plausible approaches are : Direct-
dictate a desired value to swarm members or Indirect- control or
influence one or more swarm members relying on existing control laws
to propagate that influence. Both approaches have been followed by
HSI researchers. The Indirect case uses standard consensus methods
where the operator exerts influence over a few robots and then the
swarm reaches a consensus based on its intrinsic rules. The Direct
method corresponds to flooding in which the operator directly sends
the intention to a subset of the swarm and the command then propagates
through the remainder of the swarm as a privileged message. In this
paper we compare these two methods regarding their convergence time
and properties in noisy and noiseless conditions with static and
dynamic graphs. We have found that average consensus method (indirect
control) converges much slower than flooding (direct) method but it
has more noise tolerance in comparison with simple flooding
algorithms. Also, we have found that the convergence time of the
consensus method behaves erratically when the graph's connectivity
(Fiedler value) is high.
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