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Decentralized collaborative fleet of AGVs Elham Mirzaei 2019, September

Decentralized collaborative fleet of AGV - AI MONDAYai-monday.de/.../uploads/2019/09/Decentralized-collaborative-fleet-of-AGV.pdfCAC: Collaborative AGV Controller ROS: Robot Operating

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Page 1: Decentralized collaborative fleet of AGV - AI MONDAYai-monday.de/.../uploads/2019/09/Decentralized-collaborative-fleet-of-AGV.pdfCAC: Collaborative AGV Controller ROS: Robot Operating

Decentralized collaborative fleet of AGVs

Elham Mirzaei 2019, September

Page 2: Decentralized collaborative fleet of AGV - AI MONDAYai-monday.de/.../uploads/2019/09/Decentralized-collaborative-fleet-of-AGV.pdfCAC: Collaborative AGV Controller ROS: Robot Operating

Intralogistics• Material flow• Warehousing

Challenges• Rapid change in the market

• Less labor availability

• Safety issues for the workers

• Increase in demands for “Batch Size 1” production

• Increasing demands on quick / fast production reconfiguration in complex setups

Indoor transportation solutions

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Intralogistics solutions

Fixed Material Flow

Flexible Material Flow

ProductType A

Machine A

ProductType B

• Flexible Work Flow

• Scalability

• Resilience

• Less Space Required

• Increase in Productivity

• Less Personnel Cost

• Increased Safety in the Workplace

AGV: Automated Guided Vehicle

Machine A

Machine B

ProductType A

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Product evolution

ü Cooperative Systems

20122012

v Automation

Page 5: Decentralized collaborative fleet of AGV - AI MONDAYai-monday.de/.../uploads/2019/09/Decentralized-collaborative-fleet-of-AGV.pdfCAC: Collaborative AGV Controller ROS: Robot Operating

InSystems proANT AGVs

Ø Natural navigation

Ø Easy integration to new environment

Ø Fleet scalability

Ø Flexible load capacity (between 30 and 1.200 kg)

Ø Central controller software for fleet

management

Ø Battery management system

proANT 436 proANT 016

proANT 490proANT S.A.S.H.A.

Trash-Robot

proAnt 476

proANT 485

proANT 390

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Product evolution

ü Cooperative Systems

vAutomation

Collaborative Systems

vNarrow AI

20122012 2018

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Collaborative group of AGVs

Ø Plug & play at runtime

Ø Decentralized fleet management

• No single point of failure

Ø Sharing information among the System Group

• Dynamic Obstacle

• Status Information (Battery Value, Driven km,…)

Ø Distributed decision making in the job assignment

vForm an adaptive IoT system group

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3. „I will take over the task, because I am the cheapest one“

Machine execution system

Production unit#1

AGV#1 AGV#2 AGV#3

Delivery unit

Charging unit#1

1. „I need someone to pick up 20 kg and transport it to delivery unit“

2. „I am AGV#1 an can be there in 2 minutes with costs of 10 EUR “

2. „I am AGV#2 an can be there in 5 minutes with costs of 20 EUR “

2. „I am AGV#3 an can be there in 15 minutes with costs of 5 EUR “

COATY Agent

COATY Agent

COATY AgentCOATY AgentCOATY Agent

Communication framework

Collaborative IoT scenario

Task#1:Capacity: 20 kg Source: Production unit#1Destination: delivery unit#1

Communication framework

Communication framework Communication framework Communication framework

Communication framework

Communication framework

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Narrow AI to develop CAC logic

Ø Job assignment via negotiation• Bidding function

Ø Job execution • Transport job• Charge job

Ø Strategies to prioritize and fulfill multilevel goals

• Global goal: Group of AGVs• Local goal: Individual AGV

Ø Shared driving destinations • Avoid conflict

CAC: Collaborative AGV controller

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Who wins the bidding?

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Strategy and cost function

KPI: Key Performance Indicators

Plant manager requirements:I want my systems to do the coming jobs as fast as possible.

Local goal: Minimum threshold for charge must be 20%

Global goal:Robots with minimum distance to the load position must win the job

Bidding parameters:

P1: Sate of chargeP2: Distance to the source

Bidding Strategy :

𝑪𝒐𝒔𝒕 𝒇𝒖𝒏𝒄𝒕𝒊𝒐𝒏 = (𝒘𝟏⋅ 𝒑𝟏) + (𝒘𝟐⋅ 𝒑𝟐)

𝑾𝒊𝒏𝒏𝒆𝒓 =𝑴𝒊𝒏𝒊𝒎𝒖𝒎8𝒋:𝟏

𝒎

𝒘𝒋 ⋅ 𝒑𝒋

Ø Strategy determines who is the winner.

Ø „Cost Function“ conveys all the bid parameters.

Ø Goals can be controlled over the weight of its parameter.

Ø AGVs always compare their cost for the bidding.

Requirement System goals Bid parameter Strategy KPI

KPI:1. Battery state of charge

2. Timeframe between

publishing the job and

executing the job.

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Job processing

CAC: Collaborative AGV Controller ROS: Robot Operating System

Job listAGV # 1Biddding Job execution

Job 1Win job 3

Add to the list

• Each AGV has individual job list.

• The winner add the job to it’s list.

• Jobs handed over to the execution function

from top to bottom.

• Charge job has always the highest priority

among all other jobs.

• When the job is executed, it is removed

from the list.

Job 1 begins!

Job 3

Job 2

Job 1 done!

Page 13: Decentralized collaborative fleet of AGV - AI MONDAYai-monday.de/.../uploads/2019/09/Decentralized-collaborative-fleet-of-AGV.pdfCAC: Collaborative AGV Controller ROS: Robot Operating

How to avoid conflict for shared driving destinations?

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Shared target management

• AGV requests the status of target before driving:

• Occupied: Wait

• Free: Drive

• Each target has at least one associated waiting

spot nearby

vAGVs negotiate who drives to target and who drives to waiting spot?

Waiting Spot for charge station 1

AFAP: As fast as possible

Local goal: charge if SOC< 20%Strategy:Jobs done AFAP.

I want to drive to the charge

station 1.My cost is 5$

Local goal: charge if SOC< 40%Strategy:Jobs done AFAP.

I want to drive to the charge

station 1.My cost is 10$

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Token approach

Target 1 Associated Waiting Spots

Go to T1

Negotiating:AGV#1 is bid winner: receives T1 token

AGV#2 broadcasts T1W1 as new local target & Claims token

T1

AGVs receives task & broadcasts to fleet

T1

AGV#1AGV#2 AGV#3

AGV#4

AGV#1 AGV#2

Task

T1W1

If no token exit, generate one

tT: Target for task 1

T1W1: associated waiting spot for target 1

T1

Target#1

TW1

Job execution

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Virtual demonstration

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AGV#3Conveyor

Page 19: Decentralized collaborative fleet of AGV - AI MONDAYai-monday.de/.../uploads/2019/09/Decentralized-collaborative-fleet-of-AGV.pdfCAC: Collaborative AGV Controller ROS: Robot Operating

Product evolution

ü Cooperative Systems ü Collaborative Systems

vNarrow AI

2012

Self-Organizing & Self-Optimizing systems

vGeneral AI

2012 2018 2020

v Automation

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02468

10Parameter 1

Parameter 2

Parameter 3

Parameter 4

Parameter 5

Parameter 6

Goal Parameters

Self-organizing & self-optimizing systems

Ø Self-Awareness

Ø Gathering information of all agents (Big-Data)

Ø Methods of Deep Learning

Ø Predictive maintenance

Ø Predictive work flow pick time

Ø Allows a better workload and degree of capacity utilization

of transport robot fleet

Ø Optimize the workflow and performance of the fleet by

adapting to recurring patterns in the work cycle in the

factory

01234567

1,01 1,02 1,03 1,04

Jobs per Robot per day

Robot 1 Robot 2 Average

Pick time

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Our next steps

1) AGV : Automated guided vehicle 2) POC : Proof of concept 3) ROI : Return of invest

• Turn decentralized collaborative transport robots into reality

• Develop self-optimizing robots by using general AI methods

• Mixed reality technology for system validation and strategy optimization analysis

• AGV1 prosumer POC2

• Autonomously working devices generate their own ROI3

Page 22: Decentralized collaborative fleet of AGV - AI MONDAYai-monday.de/.../uploads/2019/09/Decentralized-collaborative-fleet-of-AGV.pdfCAC: Collaborative AGV Controller ROS: Robot Operating

www.insystems.de

Elham Mirzaei

Thank you

www.proant.de