Planning to fail - Reliability needs to be considered a priori in multirobot task allocation

The reliability of individual team members has a substantial and complex influence on the success of multirobot missions. When one robot fails, other robots must be retasked to complete the tasks that were assigned to the failed robot. This in turn increases the likelihood of these other robots failing, since they have more work to do. Existing multirobot task allocation systems consider robot failures only after the fact-by replanning after a failure occurs. We hypothesize that it should be important to consider robot reliabilities when generating an initial plan. In this paper we test this hypothesis in the context of the multirobot exploration problem. We take a simple exhaustive planner and compare the plan it chooses against the optimal plan that takes into account robot failures and the backup plans that occur after failure. Our results show that for this problem domain, making an initial plan without regards to individual robot reliabilities results in choosing a suboptimal plan most of the time, and that the difference in mission performance between the chosen plan and the optimal plan is usually substantial. In brief, in order to successfully plan we must 'plan to fail'.