camera-guided agv‘scam-agv.com/cameraguidedagv_1.1.pdf · load capacity: up to 500 kg –...

camera-guided AGV‘sDeep Machine Learning with Computer Vision

.Version 1.1

Changing paradigm

• Production and warehouse are very complex environments• The recognition of environment is complicated, partly impossible• Navigation technique on the vehicle is complex, expensive and in many

cases insufficient


Changing paradigms

Logivations and A&A-Approach:• The operation area of the AGV’s is stored as 3D-Model in W2MO• Cameras monitor the whole operation area• Computers, directly connected with cameras, identify all relevant objects and report exact positions• The control computers calculate the paths of the vehicle on the basis of the position information and 3D models

of the environment and submit the control commands• The control computers can access the optimization server, which calculates optimal tours and paths on the basis

of complex mathematical algorithms • The vehicle control system must only steer the drives, no navigation on the vehicle needed• The system allows a highly redundant and fail safe configuration: − Several cameras can monitor the same objects− The control commands can be calculated simultaneously by several control computers for the same vehicle

and submitted to the vehicle• The navigation defines the absolute positions, i. e. automatic driving and manual pushing can be

arbitrarily combined. The AGV can immediately automatically continue the movement from each position. The AGV can be also tracked during the manual operation.

• The system is safe:− People or other vehicles are identified and possible collisions are early noticed and thus avoided. All the

necessary safety equipment is on board as well.© 2010-2018 Logivations GmbH. All Rights Reserved

Camera-guided AGV’s: the innovation

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W2MO 3D model

© 2010-2018 Logivations GmbH. All Rights Reserved

The operation area for the AGV is stored as a true-to-scale 3D-Model in W2MO. Driving paths can be calculated and changed with a few clicks.

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W2MO 3D model in cooperation with cameras


Сameras monitor the operation area

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Camera calibration in W2MO 3D Model


Automatic camera calibration provides high precision localization – depending on the distance and camera quality the precision of the localization varies from 1 mm to 10 cm.

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Connected and redundant computers calculate the driving paths

Computers, which are directly connected with the cameras, identify all the relevant objects and reporttheir exact position.

The control computers calculate the paths of the AGV on the basis of the position information and 3Dmodels of the environment and submit the control commands

The control computers can access the optimization server, which calculates optimal tours and pathson the basis of complex mathematical algorithms

Direct integration with WMS (e. g. SAP), movement commands can be feeded with logical warehouse locations



Camera-guided AGV‘s

W2MO uses mathematical algorithms for optimal product placement and for calculation of theoptimal tours of the AGV‘s.

Usage

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0 20 40 60 80 100Picking tours W2MO

Tour 1

Tour 2

Tour 3

Tour 4

Tour 5

Tour 6

Tour 7

Tour 8

Tour 9

Tour 10

Tour 11

Tour 12

Tour 13

Tour 14

• The integrative view of the combinedpicks allows the optimal grouping of theorders

• No sequence according to rows and drawers

Reduction of the tracks to approx. 18 % and the process times to 11 % with the help of combinatorial algorithms

• Focus on the technical conditions leadsto the signifacant increase of thepotential (e. g. dynamis number ofboxes and prodacts)

• Limited only by physical conditions

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AGV’s have only simple driving controls, no navigation on the vehicle

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People or other vehicles are identified and possible collisions are early noticed and thus avoided.

The AGV can immediately automatically continue the movement from each position.

The AGV can be also tracked during the manual operation. Other objects (e. g. people or obstacles) are also identified and can be considered

in the navigation/guidance system Manual guidance of the vehicle from the control center


Absolute navigation

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Absolute navigation: automated drives and manual pushing are differently combined

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Avoidance of collisions

People and other vehicles are identified and possible collisions are early noticed and thus avoided.

If e. g. people are identified, the driving speed can be decreased All the necessary safety equipment is on board as well


The system provides maximal safety

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Collision of the forklifts


Today: restricted view due to the navigation on the vehicle

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Collision of the forklifts

© 2010-2018 GmbH. All Rights ReservedLogivations

Everything is monitored, collisions are securely avoided. Timely warning is possible!

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Camera 1 view Camera 2 view

Forklift 1Forklift 1

Forklift 2

Collision with peopleEverything is monitored, people are also found (head recognition).

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System safety


The system is fail safe and redundantly configured, several cameras can monitor the same section

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Left camera viewRight camera view



AGV’s can be flexibly adapted to different usage conditions.

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Various Constructions

Load capacity: up to 500 kg – expandableMax speed: 0,8 m/s up to 1,6 m/s

Navigationone camera per 100 to 400 m2

Costsapprox. 15.000 to 25.000 € per one AGV

ReliabilityBoth cameras and computer system are fail safe redundantly configured



Example: benefit estimation

• Investment per AGV approx. 15.000 – 25.000 € (incl. hardware, software, project)

• Regular support fee per year approx. 8% of the investment, approx. 1.200 – 2.000 € per AGV

• Pick costs per year: ca. 40.000 €, 50% of the working time is travel time, 2/3 of the traveltime can be saved=> Saving 13.200 € / year / FTE in shift operation

• The AGV is used in 2 shifts: possible savings 26.400 € / year

• Pay Back of the investment is thus from 1 up to 2 years

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Volkswagen names Logivations as „Top-Innovator for Logistics Innovations“

• At the „Innovative Logistics Solution Day“ Logivations presented the Realtime Object Identification to more than 230 VW-Managers from 18 countries

• From more than 170 applications Logivations was selected during a multi-stage process as “Nominated Supplier” and awarded in the further selection process as“Top Innovator”.

Innovative Projects – Automotive


Megatrends explained

Computing power• Moore`s law: disproportionately growing technical progress

• GPU computing: main processors (CPU) have 2-8 cores, current GPUs - more than 2.000. CPUs excel in sequential tasks, while GPUs are designed for extremely parallelized applications.

Techniques• Machine Learning (ML): artificial intelligence derives knowledge from experience. The computer does

not get patterns and structures as inputs but signals and results. The structural link develops the learning algorithm.

• Deep Learning: algorithmic imitation of the functioning of the human brain. Over several stages links are created and results are generated from input data.


Origin of the object identification in Logivations

• The first application of the object identification in the personality protection function through the face recognition and pixelization of the W2MO`s video-based process studies.

• The image (video-frame) is identified in approx. 0.01 up to 0.1 seconds.


Deep Machine Learning: Neural networks automatically "learn" the patterns required for object identification.

Extensive databases with training data enable previously unreached identification rates (outperforming human decision makers).

Once learned patterns can be transferred to other tasks -> in practice, only a few sample images are sufficient.

Very quickly: < 0.1 seconds per image

Technical backgroundNeuronal Networks – object identification as in the human brain


Algorithm is trained on the sample images Automatically extracts ("learns") the required patterns Logivations Deep Machine Learning enables fast and reliable identification with the

help of ordinary cameras

Technical backgroundPractical example of neural networks


camera-guided agv‘scam-agv.com/cameraguidedagv_1.1.pdf · load capacity: up to 500 kg –...

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