Automated Picking System Employing a Drone
| Autor: | Federico Corò, Cristina M. Pinotti, Anil M. Shende, Francesco Betti Sorbelli |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science 0102 computer and information sciences 02 engineering and technology 01 natural sciences Drone Euclidean distance 010201 computation theory & mathematics Position (vector) Drone flies 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) DCOSS |
| Popis: | We study the possibility of using drones to implement an automated picking system in a warehouse. We imagine a warehouse divided into two contiguous areas: in one area, the drone moves according to the Euclidean distance, while in the other area, the drone moves according to the Manhattan distance. For each customer-order (CO), the automated picking system is in charge of gathering the items requested in the CO to a predefined location where the cart of the drone is positioned. For each item of the order, the drone flies to the location where the item is stored, grasps it, and brings it back to its cart. Our goal is to find the position of the drone's cart that minimizes the sum of the distances traversed by the drone to pick-up all the items of the CO. We propose algorithms to find such a location when the items to be collected are in Euclidean and Manhattan areas. We can prove a √2-approximation factor for our solutions. Moreover, we compare the efficiency of the automated picking system employing a drone with that of a traditional picking system employing a worker that pushes a cart, and we find under which conditions the drone can be more efficient. |
| Databáze: | OpenAIRE |
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