Multi-robot Scheduling for Environmental Monitoring as a Team Orienteering Problem

In this paper, we propose an evolutionary algorithm for solving the multi-robot orienteering problem where a team of cooperative robots aims to maximize the total information collected by visiting a subset of given nodes within a fixed budget on travel costs. Multi-robot orienteering problems are relevant to applications such as logistic delivery services, precision agriculture, and environmental sampling and monitoring. We consider the case where the information gain at each node is related to the service time each robot spends at the node. As such, we address a variant of the Orienteering Problem where the collected rewards are a function of the time a robot spends at a given location. We present a genetic algorithm solver to this cooperative Team Orienteering Problem with service-time dependent rewards. We evaluate the approach over a diverse set of node configurations and for different team sizes. Lastly, we evaluate the effects of team heterogeneity on overall task performance through numerical simulations.