3D vision and AI for
robot guidance
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Partners
GUIDEnow
The GuideNOW system enables smarter industrial robots thanks to adaptive real-time robot guidance based on AI and 3D vision.
Flexible
Seamless decision-making enables dynamic trajectory changes for any environment.
Reliable
Cutting-edge AI-driven processing ensures accurate real-time robot guidance.
Autonomous
GuideNOW enhances autonomous robotics, enabling real-time adaptation in unpredictable environments.
OUR BENEFITS
Reduces cost to automation
By reducing the need for these custom jigs and indexing tooling, manufacturing processes can become more efficient and cost-effective.
Reduced programming time
GuideNOW can be set in less than 30 minutes. The user can train every new reference by himself.
Improves line productivity
GuideNOW guidance is real-time while other solutions take several seconds per positioning.
Reduces non-quality
The adaptation of the robot trajectory prevents collisions with the part or the execution of processes in the wrong place.
Select a benefit
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Reduces cost to automation
Reduces programming time
Improve line productivity
Reduces non quality
TECHNOLOGY
Inbolt has developed today’s most efficient AI-powered system for the real-time matching of vision sensor data with CAD models or 3D scans.
The AI processes massive amounts of 3D data at high frequency. It identifies the position & orientation of a workpiece and enables the adaption of the robot's trajectory in real-time. This core software is 100x faster than state-of-the-art algorithms and is hardware agnostic.
HARDWARE SET-Up
Mount our 3D camera on your robotic arm.
STUDIO SET-UP
Upload the 3D model of the workpiece & train your AI.
Runtime
GuideNOW adapts the trajectory of your robot in real-time.
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USES CASES
Robot guidance for all types of manufacturing processes
Assembly
Tightening, glueing, etc.
Handling
Picking, machine tending, etc.
Others
Quality control, finishing operations, etc.
Last news & events about inbolt
Articles
Sim2Real Gap: Why Machine Learning Hasn’t Solved Robotics Yet
The most successful areas of application for deep learning so far have been Computer Vision (CV), where it all started, and more recently, Natural Language Processing (NLP). While research in Robotics is more active than ever, the translation from research to real-world applications is still a promise, not a reality. But why?
Articles
Exploring the potential of LLMs in robotics: an interview with Louis Dumas
Robotics has always been at the forefront of technological advancement, but translating human ideas into robotic actions remains one of its greatest challenges. In an interview with Louis Dumas, co-founder and CTO of inbolt, he shared his insights into the transformative potential and current limitations of Large Language Models (LLMs) in the robotics field.
USES CASES
Robot guidance for all types of manufacturing processes
Assembly
Tightening, glueing, etc.
Handling
Picking, machine tending, etc.
Others
Quality control, finishing operations, etc.