# Multi-Robot Pickup and Delivery via Distributed Resource Allocation

arXiv: https://arxiv.org/abs/2104.02415 · HTML: https://mlsanity.com/paper/2104.02415/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2021.

## Built on by (most cited first, 4 of 4)

- [Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements](https://mlsanity.com/paper/2503.02437/) (2025)
- [Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning](https://mlsanity.com/paper/2412.19538/) (2024)
- [ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments](https://mlsanity.com/paper/2502.19892/) (2025)
- [Pro-Routing: Proactive Routing of Autonomous Multi-Capacity Robots for Pickup-and-Delivery Tasks](https://mlsanity.com/paper/2503.24325/) (2025)

## Abstract (arXiv)

In this paper, we consider a large-scale instance of the classical Pickup-and-Delivery Vehicle Routing Problem (PDVRP) that must be solved by a network of mobile cooperating robots. Robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This results in a large, challenging Mixed-Integer Linear Problem that must be cooperatively solved % without a central coordinator. We propose a distributed algorithm based on a primal decomposition approach that provides a feasible solution to the problem in finite time. An interesting feature of the proposed scheme is that each robot computes only its own block of solution, thereby preserving privacy of sensible information. The algorithm also exhibits attractive scalability properties that guarantee solvability of the problem even in large networks. To the best of our knowledge, this is the first attempt to provide a scalable distributed solution to the problem. The algorithm is first tested through Gazebo simulations on a ROS~2 platform, highlighting the effectiveness of the proposed solution. Finally, experiments on a real testbed with a team of ground and aerial robots are provided.