Tractonomy Robotics, based in Belgium, develops next-generation autonomous mobile robots aimed at improving material cart handling in intralogistics, addressing the challenges of existing manual and outdated automated systems. Their robots feature advanced AI-powered navigation, can tow up to 650kg, and are supported by a cloud-based maintenance service to ensure zero downtime operations. The project has achieved notable technical milestones but encountered challenges such as network delays and limited available datasets for machine learning applications.

1. DREAMBOT
Keshav Chintamani, Elias De Coninck, Geert Dorme
Mentored by Emanuel Skubowius (Fraunhofer IML)

2. Tractonomy Robotics
Tractonomy Robotics builds next-
generation autonomous mobile
robots for automatic material cart
handling in intralogistics.
We are based in Kortrijk and Aalst
in Belgium.
Keshav Chintamani,
Co-Founder and CEO
Clients and Products
Geert Dorme
Co-Founder and CMO
Mechanical and Production
Elias De Coninck
Technical Lead
Software and DevOps

3. Companies need to handle
between 100 and 50.000 carts
x 2 shifts
x everyday
manually, with tuggers
or old & slow AGVs
AS-IS SCENARIO
Cart Handling
Painful!
© Tractonomy Robotics – Not for Redistribution3

4. Compact
Autonomous Towing
Robots
1 Shown Omnit V1 proof-of-concept - 80kg lifting/200kg towing
2 Omnit V2 production - 250kg lifting/650kg towing -> 06/2021
AI-powered navigation to carts
250kg lift - 650kg capacity 1
Patent-pending fast cart docking
© Tractonomy Robotics – No Redistribution 4
Combine ATRs with our Remote Fleet
Maintenance Cloud Services for Zero-
Downtime Operations
Easy deployment in fleets
© Tractonomy Robotics – Not for Redistribution

5. © 2018 Tractonomy Robotics
Towing upto 650kg*
Fast transfer rates
Cart search using vision
On-demand Cart
Towing
Flexible transfer of carts
* Limited by applicable machine safety standards
TO-BE SCENARIO
5

6. Objectives
• Novel edge-cloud architecture for
machine learning based on the MIDIH
Reference Architecture (RA)
• Verify it works under network delays and
losses, typical in a real-world wireless
network
• Verify interest in solution with potential
end-users
T4. Machine Learning and Artificial Intelligence advanced applications in
Smart Product, Smart Factory and Smart Supply Chains management
and optimisation

7. Mapping with the MIDIH RA -
DIM Online Processing
IoT MiddleWare
DIM Security
DIM Services
DAR Visualization DAR Online Processing

8. Approach
1. Development of a cart dataset
2. Training of a Neural Network
3. Development of a Navigation Controller
4. Processing streams in cloud for pose
estimation
5. Navigation Experiments
© Tractonomy Robotics - Confidential
Information
8

9. Testing Sites
• 70 m2 @ Aalst
• 100m2 @ Langemarke
9

10. Technical KPIs
# KPI Results Observations
1 Cart silhouette
estimation
Cart detection worked
very reliably
No public datasets
so have to create
our own
2 Pose
estimation in
cloud
Solution works only
with compressed
images
Expecting to use
Point clouds in any
real-time
application is a bad
idea
3 Pose
estimation in
lossy networks
4 Pose
estimation with
delays

11. Business KPIs
# KPI Results Observations
5 Demonstrator Views COVID blocked any ability to offer live demonstrations.
However, the MIDIH results were always shown in our pitch deck and
we’ve had very positive responses including a paid study6 Interested Customers

12. Results and Conclusions
Positives
• Achieved an architecture that maps to MIDIH-RA
• ROS2 as middleware – required for AMR applications – maps to Kafka
• ML on the edge worked -> suffered from embedded CPU resource issues
• Core components are the basis of our go-to-market
Lessons Learnt
• Don’t stream point clouds for real-tome applications.
• Network losses and delays have a big impact on real-time ROS2 messages.
• No usable ML models available for point cloud – esp. specific use cases like carts

13. THANK
YOU!