We develop 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. They consider the case where the information gain at each node is related to the service time each robot spends at the node. As such, they address a variant of the Orienteering Problem where the collected rewards are a function of the time a robot spends at a given location. They present a genetic algorithm solver to this cooperative Team Orienteering Problem with service-time dependent rewards. The researchers evaluate the approach over a diverse set of node configurations and for different team sizes. Lastly, they evaluate the effects of team heterogeneity on overall task performance through numerical simulations.
Capability: T3C4B Heterogeneous Multi-Robot Systems for Modeling & Prediction of Multiscale Spatiotemporal Processes
Points of Contact: M. Ani Hseih (PI) and Ariella Mansfield
Citation: A. Mansfield, S. Manjanna, D. G. Macharet, M. A. Hsieh “Multi-robot Scheduling for Environmental Monitoring as a Team Orienteering Problem.” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), September 2021.