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Applied Innovation Industry 4.0

Robotic arms capable of perceiving, comprehending, and operating any item in any environment

Categories
Applied Innovation Industry 4.0

Robotic arms capable of perceiving, comprehending, and operating any item in any environment

For many established sectors, the previous several years have been transformative. The COVID inflection point has accelerated the trend of ICT adoption that had been steadily trickling into these traditional sectors.

Making Manufacturing Smarter

There have been a few tech adoptions in the industrial sector, but none have come close to the capabilities that deeptech (AI, IoT, and ML) has in making manufacturing smarter.

The most difficult problem in our market is to provide the best products and services at the lowest possible cost in the shortest amount of time. IoT and AI are opening up new options for the sector to improve service, reduce downtime, and raise efficiency while lowering production costs.

Manufacturers may access additional assets, acquire business insights from accurate data in real-time, and improve day-to-day operational efficiency and production performance using AI & IoT apps and sophisticated data analytics.

Robotic Innovations

Robotic innovations have had a favorable influence across fields, particularly in the industrial business. For years, industrial robots have played an important role in assisting manufacturing organizations in streamlining their workflow, closing skill gaps and addressing the labor issue, increasing production, and maintaining accuracy and consistency.

Visually Intelligent Robots

Enabling robots to conduct the Picking, Orienting, and Placing of goods directly from their containers has long been regarded as The Holy Grail of Robotics.

Visually Intelligent Robots can be the next big thing and may have a great impact on the manufacturing industry by simplifying automation. The robots now used in the manufacturing sector are unable to see and thus can not assist in Object Manipulation. AI/ML limited to just the color & depth of an object has been a challenging problem for this.

Visual Object Intelligence Platform

We have a solution for this: a visual object intelligence platform that allows industrial robotic arms to perceive, comprehend, and operate any item in unstructured surroundings.

We offer a system that adds the missing components of Visual Intelligence to Robotic Arms, allowing them to be Object aware and manage objects with more agility – adjusting to varied forms, orientations, and weights. 

This has the potential to reduce and standardize massive, bespoke production lines into LEGO blocks of micro-factories. Some of the tasks it can perform like Picking and Placing Untrained Objects from Any Untrained Picking and Placing a Variety of Objects Orientation.

Our robots can work on a wide range of items without any prior training thanks to the patented vision and intelligence layers. This serves as the foundation for universal object manipulation and, by extension, labor automation.

The platform is driven by modern technology and can distinguish between sight and vision. It enables robots with human-like eyesight and adaptability to grab even Mirror-Finished items without any pre-training (a feat that existing ML systems are incapable of accomplishing). It employs technologies such as Auto-Focus Liquid Lens Optics, Optical Convergence, Temporal Imaging, Hierarchical Depth Mapping, and Force-Correlated Visual Mapping for achieving this result.

These intelligent robots can grasp the aspects of an item and re-orient them based on their needs. The AI and Machine Learning algorithms assist robotic arms in processing tasks even in unstructured environments and aligning them in the best way feasible. These are also cost-effective and robust, and dependable.

The platform can find application in the manufacturing sector and may also assist warehouses, logistics, and industrial kitchens streamline duties. To know more about this solution and for product demo please write to us at open-innovator@quotients.com

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