| dc.description.abstract | Some problems are best solved by systems of multiple robots, in which each robot is
assigned one task. A multi-robot system can, upon the start of a series of tasks, compute the
optimal task allocation for best performance of the team. For certain systems, during runtime,
changes in the environment, tasks, and state of individual robots might change which allocation
of tasks to robots is optimal, and the performance of the team would improve if the robots
switched tasks. Because communication between robots is expensive, in some cases it is better to
calculate an interval in which changes in the environment, tasks, and robots are not significant
enough to render the original allocation suboptimal. This way, robots only initiate
communication and correction if the system is likely to switch tasks, which limits the costs of
communication and computation in the system. In the problem of task allocation of single robot,
single task cases where environments and thus optimal assignments are expected to vary over
time, some knowledge of the system might help reduce computation and make possible a more
scalable algorithm for determining cost changes. In some systems, some costs may be known not
to vary over time. This research proposes creating and analyzing cost matrices of assignments to
examine if taking advantage of the certainty of some variables will improve performance. If
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successful, models for exploiting certainty of task allocation will take less computation than
calculating ranges for all variables, and will save resources during runtime. | en |
