Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Systems

Abstract

In this paper, we propose a distributed multistage optimization method for planning complex missions for heterogeneous multi-robot systems. 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 utilize a distributed metaheuristic based on evolutionary computation to allocate tasks and generate schedules for the set of chosen decompositions. The solution is evaluated in a simulation setup of an automated greenhouse use case, where we demonstrate the system’s ability to adapt the planning strategy depending on the available robots and the given optimization criteria.