# Context-Aware Route Planning for Automated Warehouses

## TL;DR

This paper presents a novel context-aware route planning approach for automated warehouses that improves throughput and reduces failure rates, effectively managing dynamic task allocation and collision-free trajectories in real-time.

## Contribution

It introduces a new algorithm for collision-free trajectory planning that handles dynamic task assignment and prioritizes throughput over makespan in automated warehouses.

## Key findings

- Lower fail rate compared to previous algorithms
- Higher quality trajectory planning results
- Maintains real-time computational efficiency

## Abstract

In order to ensure efficient flow of goods in an automated warehouse and to guarantee its continuous distribution to/from picking stations in an effective way, decisions about which goods will be delivered to which particular picking station by which robot and by which path and in which time have to be made based on the current state of the warehouse. This task involves solution of two suproblems: (1) task allocation in which an assignment of robots to goods they have to deliver at a particular time is found and (2) planning of collision-free trajectories for particular robots (given their actual and goal positions). The trajectory planning problem is addressed in this paper taking into account specifics of automated warehouses. First, assignments of all robots are not known in advance, they are instead presented to the algorithm gradually one by one. Moreover, we do not optimize a makespan, but a throughput - the sum of individual robot plan costs. We introduce a novel approach to this problem which is based on the context-aware route planning algorithm [1]. The performed experimental results show that the proposed approach has a lower fail rate and produces results of higher quality than the original algorithm. This is redeemed by higher computational complexity which is nevertheless low enough for real-time planning.