# Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams

arXiv: https://arxiv.org/abs/2109.10106 · HTML: https://mlsanity.com/paper/2109.10106/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Robotic manipulation of multiple objects as a POMDP](https://mlsanity.com/paper/1402.0649/) (2014)
- [Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs](https://mlsanity.com/paper/1703.04211/) (2017)
- [Control of Multi-Agent Systems with Finite Time Control Barrier Certificates and Temporal Logic](https://mlsanity.com/paper/1808.02393/) (2018)
- [A Coalition Formation Algorithm for Multi-Robot Task Allocation in Large-Scale Natural Disasters](https://mlsanity.com/paper/1704.05905/) (2017)
- [Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams](https://mlsanity.com/paper/2109.03089/) (2021)

## Built on by (most cited first, 1 of 1)

- [Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees](https://mlsanity.com/paper/2501.16539/) (2025)

## Abstract (arXiv)

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.