Technology

An integrated automation approach for flexible textiles

The challenge is not just moving fabric, but creating a stable, monitored and scalable process around an unstable material.

Technology setup with robot, gripper, sewing machine and camera

Core technology building blocks

Simulation of roboto sewing

Robotics

Industrial robots precisely adapted for garment handling, seam routing, and positioning.

Textile Handling

Proprietary system managing limp, deformable fabric with industrial repeatability.

AI Trajectory Planning

AI continuously optimizes robot motion per garment type — adapting in real time.

Process Control

Digital sequencing ensuring consistent quality, throughput, and production targets.

Operations & Monitoring

Live KPIs, anomaly detection, and remote diagnostics for industrial operators.

Process workflow

Unloading of a textile buffer

Material feed

Supply of textile semi-finished goods.

Pickup and positioning

Controlled gripping and alignment of flexible material.

Guided process execution

Textile support during sewing, welding, bonding or handling operations.

Monitoring and feedback

Sensor input supports process stability and traceability.

Data and production logic

Interfaces and software provide visibility beyond one isolated motion.

AI-driven textile automation

Technical process image showing DBV setup

Vision and Learning for Automation

ADOTC uses image analysis and reinforcement learning to improve robotic perception, decision-making, and process stability in textile automation.

This helps robots better understand and respond to the behaviour of flexible materials, enabling more reliable handling, guidance, and joining of textiles under changing process conditions.

Understanding Flexible Material Behaviour

The goal is to enable robots to better interpret and respond to the behaviour of flexible materials.

This is essential because textiles can deform, shift, stretch, or fold during handling and sewing, welding or bonding, making automated processing significantly more complex.

Continuous Learning in Real Production

Our robots adapt to process variation and continuously improve execution quality in real production environments.

By learning from process data and visual feedback, they can respond more robustly to changing material behaviour, tolerances, and operating conditions.

This supports more stable processes, higher repeatability, and more reliable automation in everyday textile production.

Technology Platform

ADOTC Guidingsystem on robot
ADOTC AI for robotic sewing
ADOTC Guidingsystem on robot
ADOTC magazin on robot
ADOTC cutting
Partners and funding support

Built on engineering expertise and applied development

ADOTC combines textile engineering, automation technology and industrial implementation. The company develops practical robotic sewing, welding & bonding solutions for manufacturing environments and builds on applied development supported through programmes including EXIST and DBU.

Partners and ecosystem

Funding and programme support

Next step

Let’s evaluate your textile process

Tell us about your product, production step or automation challenge. We will assess whether a robotic approach is technically meaningful for your application.