Forming an effective multi-robot team robust to failures

We are interested in forming a multi-robot team that attains high utility at a task, and is robust to failures in the robots. We consider configurable robots that are composed of modules, e.g., motors, sensors, and actuators, where each module has an independent probability of failure. The performance of the multi-robot team at the task depends not only on how the robots in the team are composed from modules, but also the probability of failure of the selected modules. We formally define the robust team formation problem, and introduce two methods of defining the optimal team. We contribute the Robust Synergy Graph for Configurable Robots (ρ-SGraCR) model, and two team formation algorithms to find effective robust teams. The first algorithm, OptRobust, runs in exponential time and finds the optimal robust team. The second algorithm, ApproxRobust, makes assumptions about the module failures and approximates the optimal robust team, and runs in polynomial time. We demonstrate the efficacy of the ρ-SGraCR model in modeling robust team performance, and evaluate ApproxRobust and OptRobust. Finally, we apply the ρ-SGraCR model to a real robot problem in the foraging domain, and show that it outperforms competing approaches.