What we do

Python code

What we do

  • Machine vision (AI)
  • Reinforcement learning (AI)
  • Anomaly detection (AI)
  • Tailored machine software
  • Data captation (IOT)
  • Data analysis
  • MQTT (IOT)
  • Rest API development (IOT)
  • PLC communication (IOT)
  • Time series prediction (AI)
  • User interface design
  • Pointcloud processing

Machine Vision (AI)

Machine vision can be used for a wide variety of industrial tasks. Some examples are sorting, advanced quality checks and object pose detection for robotic manipulation. Machine vision allows automation in a highly variable industrial environment resulting in smart software that can accomplish multiple tasks. This allows low batch high mix production instead of the high batch low mix production with fixed tasks.
With Artificial intelligence (AI) we push machines towards how humans and animals control their environment. Nowadays robots are usually moving with a "blind" open loop point to point movement. With machine vision a smart closed loop system is acquired that resembles how humans act. Using IOT our algorithms communicate with other industrial devices such as plc's and industrial computers to send the pose and quality information. Whether you want to make an advanced bin picking application or detect the quality of your strawberries, we provide a solution from A to Z.

Machine vision

Smarter machines using AI & IOT

Industrial robotic AI

Smarter machines using AI & IOT

Industrial machines are advanced electromechanical constructions but usually have a "simple" repetitive software program. Using more sensors combined with the capabilities of AI & IOT can give them incredible reasoning capabilities such that more complex tasks can be performed. Machines should be connected to allow for quick communication of the current state of events on which they can anticipate.
Advanced control algorithms to accurately control machines also find there way more and more in the industrial landscape. For many use cases the classic PID controller cannot properly control the highly non linear dynamic behaviour of most machines. To get around this we use advanced model predictive control with a deep learning driven dynamics model. This dynamics model can easily capture friction or other dynamic characteristics which are hard to model.

Data captation (IOT)

By providing IOT that allows to communicate with most industrial communication protocols (S7,ADS,modbus,MQTT,REST,OPCUA,..) data can easily be captured. This data is very useful for monitoring and visualizing the state of your machines and processes. Using the captured data advanced decision making deep learning models can be made for optimization and anomaly detection algorithms

Industrial IOT data analysis

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