Abstract: Many architectures have been proposed to solve tightly-coupled
multirobot tasks (MT) through coalitions of heterogeneous robots.
However, several issues remain unaddressed. As coalitions are formed,
sensor constraints among robots are also established. For example, in a
leader-follower task, follower robots must keep leader robots within
their sights, while in a box-pushing task, pusher robots must maintain
proper pushing positions relative to other robot teammates while
aligning their pushing direction to the goal. The question of how to
keep these constraints satisfied during the entire execution, from
initial configurations to completeness of the task, remains an open
issue. In addition, environmental factors, both static and dynamic, can
influence the maintenance of the constraints. Moreover, problems arise
when the constraints are unsatisfiable given the current circumstances.
For example, the sight of the leader might be blocked or there might be
obstacles in the pushing path. In order to create a general method to
address these issues for various applications, we propose an approach
based on measures of information quality using sampling techniques. Our
approach to the general method combines the use of sensor models,
environment sampling, measures of information quality, a motion model
with sampling, and a constraint model. To illustrate this method, we
apply the approach to solve robot tracking and navigation tasks both in
simulation and with physical robots. Experimental results illustrate
the flexibility and robustness of the approach.