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Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses

Vassilissa Lehoux-Lebacque, Tomi Silander, Christelle Loiodice, Seungjoon Lee, Albert Wang, Sofia Michel

TL;DR

This work tackles lifelong multi-agent path finding in automated warehouses under realistic robot dynamics and interdependent tasks. It introduces Interleaved Prioritized Planning (IPP) to handle inter-task dependencies and the VP* trajectory solver to compute dynamics-compliant paths through via points while avoiding moving obstacles. The authors prove completeness under simple assumptions and validate the approach with extensive simulations and real-warehouse tests, showing dynamics-aware planning is essential for safety and feasibility. The results indicate meaningful improvements over dynamics-agnostic baselines and provide a scalable framework for real-world deployment, while outlining avenues for faster replanning and joint optimization of task assignment and trajectories.

Abstract

Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy simplifications, both on the environment and the agents, which make the resulting algorithms impractical for real-life scenarios. In this paper, we consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations. This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories fulfilling these tasks. To solve this MAPF problem, we propose an extension of the standard Prioritized Planning algorithm to deal with the inter-dependent tasks (Interleaved Prioritized Planning) and a novel Via-Point Star (VP*) algorithm to compute an optimal dynamics-compliant robot trajectory to visit a sequence of goal locations while avoiding moving obstacles. We prove the completeness of our approach and evaluate it in simulation as well as in a real warehouse.

Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses

TL;DR

This work tackles lifelong multi-agent path finding in automated warehouses under realistic robot dynamics and interdependent tasks. It introduces Interleaved Prioritized Planning (IPP) to handle inter-task dependencies and the VP* trajectory solver to compute dynamics-compliant paths through via points while avoiding moving obstacles. The authors prove completeness under simple assumptions and validate the approach with extensive simulations and real-warehouse tests, showing dynamics-aware planning is essential for safety and feasibility. The results indicate meaningful improvements over dynamics-agnostic baselines and provide a scalable framework for real-world deployment, while outlining avenues for faster replanning and joint optimization of task assignment and trajectories.

Abstract

Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy simplifications, both on the environment and the agents, which make the resulting algorithms impractical for real-life scenarios. In this paper, we consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations. This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories fulfilling these tasks. To solve this MAPF problem, we propose an extension of the standard Prioritized Planning algorithm to deal with the inter-dependent tasks (Interleaved Prioritized Planning) and a novel Via-Point Star (VP*) algorithm to compute an optimal dynamics-compliant robot trajectory to visit a sequence of goal locations while avoiding moving obstacles. We prove the completeness of our approach and evaluate it in simulation as well as in a real warehouse.
Paper Structure (36 sections, 1 theorem, 2 figures, 6 tables, 2 algorithms)

This paper contains 36 sections, 1 theorem, 2 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. MAPF and MAPD for automated warehouses.
  4. MAPF with realistic assumptions.
  5. MAPF in uncertain environments.
  6. Lifelong MAPF with Interdependent Tasks
  7. Agents.
  8. Warehouse Graph.
  9. Orders and Tasks.
  10. Lifelong MAPF with Interdependent Tasks.
  11. Interleaved Prioritized Planning with VP*
  12. Task Assignment Heuristic
  13. Interleaved Prioritized Planning Algorithm
  14. Routing Multi-Graph
  15. Collision Checking
  16. ...and 21 more sections

Key Result

Theorem 1

Assume that each robot has a designated waiting place, where it can be idle without interfering with other robot trajectories. If the robots are at their waiting place at the beginning of the planning, the Interleaved Prioritized Planning algorithm is complete.

Figures (2)

  • Figure 2: Examples of warehouse layouts with the physical warehouse graph. Shelves are shown in gray, charging stations/waiting areas are shown in cyan and the workstations are shown in green.
  • Figure 3: Example of graph conversion. Original warehouse nodes are in blue, new routing graph nodes are in orange, dashed lines are straight arcs, while solid lines are turning arcs. The solid turning lines within node C represent two arcs each: for turning clockwise and counter-clockwise.

Theorems & Definitions (2)

  • Theorem 1
  • proof