# Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

arXiv: https://arxiv.org/abs/2110.14891 · HTML: https://mlsanity.com/paper/2110.14891/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2021.

## Built on by (most cited first, 30 of 30)

- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning](https://mlsanity.com/paper/2310.01207/) (2023)
- [Multi-Robot Coordination and Layout Design for Automated Warehousing](https://mlsanity.com/paper/2305.06436/) (2023)
- [Multi-Goal Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/2208.01223/) (2022)
- [Collaborative Target Search with a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach](https://mlsanity.com/paper/2204.12181/) (2022)
- [Arbitrarily Scalable Environment Generators via Neural Cellular Automata](https://mlsanity.com/paper/2310.18622/) (2023)
- [DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments](https://mlsanity.com/paper/2209.02865/) (2022)
- [Improved Anonymous Multi-Agent Path Finding Algorithm](https://mlsanity.com/paper/2312.10572/) (2023)
- [Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree](https://mlsanity.com/paper/2307.00663/) (2023)
- [Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications](https://mlsanity.com/paper/2308.10393/) (2023)
- [Compositional Coordination for Multi-Robot Teams with Large Language Models](https://mlsanity.com/paper/2507.16068/) (2025)
- [Simultaneous Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications](https://mlsanity.com/paper/2401.04003/) (2024)
- [ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem](https://mlsanity.com/paper/2404.05223/) (2024)
- [Learning Multi-agent Multi-machine Tending by Mobile Robots](https://mlsanity.com/paper/2408.16875/) (2024)
- [Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search](https://mlsanity.com/paper/2408.16890/) (2024)
- [A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation](https://mlsanity.com/paper/2607.06990/) (2026)
- [Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints](https://mlsanity.com/paper/2402.08772/) (2024)
- [A Conflict-Aware Optimal Goal Assignment Algorithm for Multi-Robot Systems](https://mlsanity.com/paper/2402.13292/) (2024)
- [Optimal Integrated Task and Path Planning and Its Application to Multi-Robot Pickup and Delivery](https://mlsanity.com/paper/2403.01277/) (2024)
- [SPACE: A Python-based Simulator for Evaluating Decentralized Multi-Robot Task Allocation Algorithms](https://mlsanity.com/paper/2409.04230/) (2024)
- [Foundation Models for Rapid Autonomy Validation](https://mlsanity.com/paper/2411.03328/) (2024)
- [Neural ATTF: A Scalable Solution to Lifelong Multi-Agent Path Planning](https://mlsanity.com/paper/2504.15130/) (2025)
- [GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding](https://mlsanity.com/paper/2512.03194/) (2025)
- [Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory](https://mlsanity.com/paper/2603.23967/) (2026)
- [ARMATA: Auto-Regressive Multi-Agent Task Assignment](https://mlsanity.com/paper/2605.04225/) (2026)
- [A Neuromorphic Reinforcement Learning Framework for Efficient Pathfinding in Robotic Mobile Fulfillment Systems](https://mlsanity.com/paper/2606.20031/) (2026)
- [Lifelong Multi-Subsystem Pickup and Delivery with Buffer-Limited Handover Stations](https://mlsanity.com/paper/2607.17724/) (2026)
- [Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shifts](https://mlsanity.com/paper/2608.21554/) (2026)
- [Integrated Optimization of Automated Warehouse Operations and Last-Mile Transport for Differentiated On-Demand Delivery](https://mlsanity.com/paper/2609.19048/) (2026)
- [Reinforcement Learning Based Heterogeneous Task Allocation Under Spatio-Temporal Environmental Uncertainty for Multi-Robot Teams](https://mlsanity.com/paper/2610.15237/) (2026)

## Abstract (arXiv)

Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of robots is tasked with transporting a set of tasks, each from an initial location and each to a specified target location. Appearing in the context of automated warehouse logistics and automated mail sortation, MAPD requires first deciding which robot is assigned what task (i.e., Task Assignment or TA) followed by a subsequent coordination problem where each robot must be assigned collision-free paths so as to successfully complete its assignment (i.e., Multi-Agent Path Finding or MAPF). Leading methods in this area solve MAPD sequentially: first assigning tasks, then assigning paths. In this work we propose a new coupled method where task assignment choices are informed by actual delivery costs instead of by lower-bound estimates. The main ingredients of our approach are a marginal-cost assignment heuristic and a meta-heuristic improvement strategy based on Large Neighbourhood Search. As a further contribution, we also consider a variant of the MAPD problem where each robot can carry multiple tasks instead of just one. Numerical simulations show that our approach yields efficient and timely solutions and we report significant improvement compared with other recent methods from the literature.