KIVA system

COORDINATING HUNDREDS

OF COOPERATIVE,

AUTONOMOUS VEHICLES IN

A WAREHOUSE

Dev Bahadur Poudel

Automation and Control Seminar

Jacobs University

Bremen

Monday, July

30, 2012

Automation and Control Seminar 1

Structure of Presentation

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Overview on Multi-Agent System

Multi Agent System in Warehouse

Kiva System in Warehouse

Autonomous Robot

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Robots that can perform desired task in

unstructured environment without human

guidance.

Have some degree of autonomy

Have control over their internal state and their

behavior

Agents

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Computer system that is capable of

independent(autonomous) action to satisfy

design objectives

Problem Solving entities

Situated in a particular environment (input

environment through sensors and act on

environment through effectors)

Multi-Agent System

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Agent Oriented Approach to solve Complex

Problem

Consist of a number of agents interacting with

each other to accomplish the goal in a

common environment

Problem are decentralized to different agents

Decomposition of Knowledge and Expertise

Distributed Intelligence

Why and Where Multi-agents

Systems?

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Decentralization of Problems and Knowledge

For Complex Control Systems

Increase the productivity and decrease the cost

Reliability, Robust

Military Network-centric Operations

Search and Rescue

Transportation and Logistics

Now days used in Warehouse to increase the efficiency

Warehouse Scenario

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Goods-to- Customer

Traditional Automation in

Warehouse

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Pickers(human) move around the Warehouse

Fetch products and return them to packing

station

Human have to look for the order items

Order items in Conveyors

Batch Processing

Time consuming

Modern Automation in

Warehouse

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Orders’ item come in hand of picker like a

MAGIC!

Use of Autonomous agents

Agents search the order fetch to the human

Agents co-ordinate to achieve a system goal

Many orders can be fulfilled

Increases Productivity

Kiva System

Kiva System

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State-of-the art for modern automation in

warehouse

Founded on better approach for order fulfillment

(goods to customers)

Uses Hundreds of autonomous mobile robots

Sophisticated control Software

Uses the concept of Distributed Intelligence

Founded in 2003 (Mick Mountz )

Fielded in 2006

Implements Distributed Intelligence

Resources

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Inventory ( with dimensions and frequency)

Inventory Pods (pods can visit one or multiple

stations)

Bins (pods can have 1-1000 bins)

Order Pods

Parking space for pods

Robots

Picking Stations

Kiva System Layout

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Green: Storage Area

Orange: Mobile Robots

Blue: Stations

MAS Architecture in

Warehouse(Kiva)

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Job Manager

Inventory Station Drive Unit

Agent 1

Agent 2Agent 3

Drive Unit Agent

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Mobile Robots

Fetch the Inventory pod to the picking station

Take the Order pods to the shipping station

Transport the Inventory Pod to replenishment

Path Planning

Inventory Station Agent(ISA)

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Picking stations : Workers pick items

Replenishment stations: barcodes are

scanned and appropriate pod come to the

station

Report accomplishment of its tasks.

Equipped with computer that pick lights,

barcode scanners, laser pointers used to

identify the pick and put locations

Job Manager

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Central Server System

Resource Allocation

Communicate with Warehouse Management

System

Receives customer orders that need to be

fulfilled

Assigns drives, pods, and stations to carry out

the tasks.

Kiva Robots and Pods

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Kiva working Mechanism

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JM receives orders and assign to stations to

fulfill

Robot(Drive Unit) carries the inventory pods

Inventory pods come to picking stations

Picking: Workers pick items and put in order

pod

Shipping : Order pod move for shipping after

fulfilling all the orders

Replenishment : Inventory pods go for

replenishment

Pick-Pack-Ship

Order fetch Configuration

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Path Planning

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Optimization of the path to fetch order from

storage to the station

A* Algorithm

Travelling Salesman Problem

Resource Allocation Challenges

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Objective: Optimizing the system(keep the

workers busy minimizing the robots and pods

used)

Which order to assign to which robot?

Which pod to pick up?

Where to keep the pod after order is fulfilled?

Which pod to send for replenishment?

Optimization(make all the robots and stations

busy)

Heuristic Technique for

Optimization

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Order Allocation

1.Time to fulfill the order must be minimized

2. Inventories around the station and in

queue pods must be considered

Inventory Pod Selection

1.Nearer pods

2. Multiple items to be picked in one visit

Resource Allocation

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Pod Storage Allocation:

1.Slow frequency pods kept backward

2.high frequency pods are kept nearer

Robot Allocation:

1.Pick More pods using less Robots

2.Decrease the Queue in stations

Replenishment Allocation:

1.Maximize the cubic utilization of the pods

2.Bin packing Problem

3.Create faster pods and slower pods

Advantages

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Increase Productivity: double the output

Lower Cost: lower installation and operational

costs than traditional warehouse automation

systems

Location free replenishment : items can be

kept in any pods

Adaptive storage: Pods are store using

heuristic

Expandability :Add inventory pods and drive

units to increase throughput during peak

season

Challenges

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To the Computer Scientist

Development of an appropriate high level software infrastructure/framework to support the building of multi-agent systems

Efficient Algorithm for Optimization

Path Planning for robots

Resource Allocation

Coordinating Robots

Dynamic, Stochastic and hence Intractable

Kiva in Action

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Have a Nice Day!

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