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Decentralized collaborative fleet of AGVs
Elham Mirzaei 2019, September
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
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
Product evolution
ü Cooperative Systems
20122012
v Automation
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
Product evolution
ü Cooperative Systems
vAutomation
Collaborative Systems
vNarrow AI
20122012 2018
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
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
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
Who wins the bidding?
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.
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!
How to avoid conflict for shared driving destinations?
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$
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
Virtual demonstration
AGV#3Conveyor
Product evolution
ü Cooperative Systems ü Collaborative Systems
vNarrow AI
2012
Self-Organizing & Self-Optimizing systems
vGeneral AI
2012 2018 2020
v Automation
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
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
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