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Robotics
AI in Robotics
Benchmark | Design | Prototype
AI in Robotics
Benchmark | Design | Prototype
Widespread adoption of robots
There has been increasing adoption of robots across food production, retail, healthcare, and distribution operations, and it may be even closer to day-to-day applications. According to IFR, from 2020 to 2022 almost 2 million new units of industrial robots are expected to be installed in factories around the world.
One of the key trends is how AI in robotics and computer vision will benefit robot implementation. Digital sensors combined with smart software allow direct teaching methods, like “Programming by Demonstration” leading to faster installation and quicker adoption of industrial robots as the time for programming customized movements reduces. Other major trends in robotics are increased adoption of robots–as–a–service model, cloud robotics, collaborative robots, and delivery robots.
Opportunities & Challenges
With the RAAS model becoming popular for SMEs, it becomes challenging for OEMs to provide customized systems for niche industries
OEMs have the opportunity to build self-learning robots that use machine vision to learn from the operators, can automatically generate a map of their surroundings, and navigate themselves within a dynamic environment. These will be the differentiators that will increase the adoption of AI in robotics.
AI in Robotics Service Framework
Software Modules
- Lidar Processing
- Localization & Mapping
- Navigation & Control
- Vision Processing
AI in robotics
- Implementation of Machine Vision using deep neural network and predictive analytics for object detection, identification, face recognition, and other AI features.
- Expertise in working on industrial exploration and home appliance robotics showcasing the benefits of AI in robotics.
Robot design
- Aesthetic Styling of the Robotic Arm.
- Enclosure design and detailing
- Engineering detailing for mechanical structural members
- Colours, Materials Finish and Graphic Design
Differentiators
- Vision-based robot implementation using deep neural networks for Object Detection, Object Identification, Face Recognition, and other AI features.
- Expertise in working on industrial exploration, AFV, and home appliance robotics
- Ready mapping and navigation system that has been proven in an autonomous driving scenario
- Leveraging predictive analytics for enhanced robot performance and efficiency
Benefits to the Customer
- Single stop vendor for conceptualizing to prototyping of mobility robots
- Software testing automation frameworks that reduce defined testing time by 40%
- Enhanced efficiency and reduced operational costs through the integration of AI in robotics and predictive analytics
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