Kiva-Paper
perAltering and Improving Kiva
Some suggestions for improvement of the current Kiva system
Marcel Flipse
1473379
2011
Vrije Universiteit, Artificial Intelligence Department,
De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands, marcel.flipse@gmail.com Course: Bachelor Referaat , supervisor: Zhisheng Huang
Abstract. Kiva has improved overall performance of distribution centers by implementing robotics and concepts from Artificial Intelligence.
Although the results look promising, there is still some room for improvement. Small changes in the environment or robot behavior rules might improve performance once more. On the other hand results of fundamental changes like introducing chaos are much more uncertain. This paper sketches some changes that could improve the performance of the current Kiva systems.
1
Introduction
This paper starts with a short introduction on the Kiva system, a definition of some basic concepts and an explanation of control methods. After the introduction some changes to the Kiva system are given. Readers should have an interest in automated warehouses and robotics. Basic knowledge of computer systems might be useful.
1.1
Kiva
The Kiva system [9, 2] is a solution for automating pick, pack and ship orders used in distribution centers. It takes over product search and product retrieval assignments from human workers. Instead of applying technology to fit robots in an existing system, Kiva uses a custom environment.The floor of the distribution center is equipped with a barcode grid and small orange robots that fit underneath special product racks. A central server sends a message to a particular robot to perform a product retrieval task. This message contains the coordinates on the grid, which are barcode-stickers and information which specific sub part of the rack contains the needed product. The robot is equipped with a barcode laser so it can read its current position. The robot then calculates a path by
Some suggestions for improvement of the current Kiva system
Marcel Flipse
1473379
2011
Vrije Universiteit, Artificial Intelligence Department,
De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands, marcel.flipse@gmail.com Course: Bachelor Referaat , supervisor: Zhisheng Huang
Abstract. Kiva has improved overall performance of distribution centers by implementing robotics and concepts from Artificial Intelligence.
Although the results look promising, there is still some room for improvement. Small changes in the environment or robot behavior rules might improve performance once more. On the other hand results of fundamental changes like introducing chaos are much more uncertain. This paper sketches some changes that could improve the performance of the current Kiva systems.
1
Introduction
This paper starts with a short introduction on the Kiva system, a definition of some basic concepts and an explanation of control methods. After the introduction some changes to the Kiva system are given. Readers should have an interest in automated warehouses and robotics. Basic knowledge of computer systems might be useful.
1.1
Kiva
The Kiva system [9, 2] is a solution for automating pick, pack and ship orders used in distribution centers. It takes over product search and product retrieval assignments from human workers. Instead of applying technology to fit robots in an existing system, Kiva uses a custom environment.The floor of the distribution center is equipped with a barcode grid and small orange robots that fit underneath special product racks. A central server sends a message to a particular robot to perform a product retrieval task. This message contains the coordinates on the grid, which are barcode-stickers and information which specific sub part of the rack contains the needed product. The robot is equipped with a barcode laser so it can read its current position. The robot then calculates a path by
References: International Conference on Robotics and Automation, volume 3, pages 2312–2317, 1997. http://marcel.flipse.itwillwork.nl, 2011. 6. Martijn C. Schut. Scientific handbook for simulation of collective intelligence. online, http://www.cs.vu.nl/ schut/dbldot/collectivae/sci/sci.pdf, 2007. joint conference on Autonomous agents and multiagent, AAMASS ’05, pages 67–74, 2005. 8. Michael Wooldridge and Nicholas R. Jennings. Intelligent agents: theory and practice. The Knowledge Engineering Review, 10(2):115–152, 1995.