Sequential Stochastic Multi-Task Assignment for Multi-Robot Deployment Planning

Real-time sequential decision making under uncertainty is a challenging task for autonomous robots. Such problems are even more challenging when making decisions involving heterogeneous teams of robots completing multiple tasks. Deploying autonomous taxi cabs and utilizing drones for package delivery represent relevant examples of these types of problems. In this paper, we present an effective solution to a multi-robot multi-task sequential stochastic assignment problem using a simulation-based optimization algorithm (MARP). Our algorithm employs a novel approach that uses Monte Carlo simulation to seek the deployment with the highest probability of being optimal. To demonstrate MARP's performance and robustness, we performed more than 2,000 numerical experiments in two different problem domains, evaluating MARP's performance against three different comparison algorithms. These numerical studies show that MARP significantly outperforms the comparison methods, achieving results within 5% of the maximum possible reward.