Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams
This addresses mission planning for heterogeneous multi-robot teams in domains like agriculture, but it appears incremental as it builds on existing optimization and evolutionary methods.
The paper tackled the problem of planning complex missions for heterogeneous multi-obot teams by proposing a distributed multi-stage optimization method, which was evaluated in a simulated automated greenhouse use case to demonstrate adaptability based on available robots and optimization criteria.
In this paper, we propose a distributed multi-stage optimization method for planning complex missions for heterogeneous multi-robot teams. This class of problems involves tasks that can be executed in different ways and are associated with cross-schedule dependencies that constrain the schedules of the different robots in the system. The proposed approach involves a multi-objective heuristic search of the mission, represented as a hierarchical tree that defines the mission goal. This procedure outputs several favorable ways to fulfill the mission, which directly feed into the next stage of the method. We propose a distributed metaheuristic based on evolutionary computation to allocate tasks and generate schedules for the set of chosen decompositions. The method is evaluated in a simulation setup of an automated greenhouse use case, where we demonstrate the method's ability to adapt the planning strategy depending on the available robots and the given optimization criteria.