Context-Aware Route Planning for Automated Warehouses

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 throughputthe 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.

Human Intention Recognition for Human Aware Planning in Integrated Warehouse Systems

“…Note that we assume that robots are equipped with a safety system ensuring that the robots will stop if they come to a range that is too close to the human worker. The multi-robot warehouse simulator was originally presented in [20], while for the current paper we have extended the simulator with the ability to include human worker plan deviation.…”

“…1) Multi-robot route planning: In this section we leverage the planning method we proposed in [20] that is based on the CARP algorithm [7]. The original algorithm structures its map as a resource graph, where each resource has a corresponding timeline.…”

On Randomized Searching for Multi-robot Coordination

“…The experiments show that the CARP algorithm is superior in all measured qualities. In our recent paper [14], we propose a modification of CARP, which generates a trajectory for an robot a k assuming that trajectories for k1 robots are already planned which can possibly lead to modification of those planned trajectories. The main idea is to iteratively build a set of robots whose trajectories mostly influence an optimal trajectory of a k .…”