Page 31 - Toucpad robotics C11

Example: In a large e-commerce warehouse, AMRs retrieve shelves containing ordered items and bring them to
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human pickers, significantly speeding up order fulfilment. They intelligently navigate around obstacles like fallen
boxes or human workers without needing pre-defined routes.
Predictive Maintenance and Quality Control
Concept: New Age Robotics Systems, often connected through IoT, continuously collect data on their own performance
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(e.g., motor vibrations, temperature, operational hours). This data is fed into AI/ML models to predict potential
equipment failures before they occur and to identify defects in manufactured products.
AI/ML Application: Machine Learning algorithms (especially supervised learning and anomaly detection) are trained
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on vast datasets of normal and faulty robot operation or product defects. They can identify subtle patterns that
indicate impending failure or quality issues, allowing for proactive maintenance or immediate rejection of faulty
products.
Impact: Reduced downtime due to unexpected equipment failure, lower maintenance costs, improved product
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quality, and reduced waste in manufacturing processes.
Example: A robotic arm on an assembly line might have built-in sensors monitoring its joint temperatures and
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vibration levels. An AI system continuously analyses this data. If it detects a subtle increase in vibration patterns that
it has learned to associate with impending bearing failure, it can alert maintenance staff to replace the bearing before
the robot breaks down completely, preventing costly production halts.

Healthcare Robotics
Robotics in healthcare is moving beyond traditional surgical aids to encompass a broader range of applications, leveraging
AI for greater precision, assistance, and patient care.

Advanced Surgical Robots
Concept: While surgical robots like the Da Vinci system have
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been around, New Age Surgical Robotics are incorporating
more AI for enhanced autonomy in specific tasks, improved
precision, and better decision support for surgeons.
AI/ML Application: Computer Vision and ML algorithms can
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assist surgeons by identifying critical anatomical structures,
providing real-time data overlays, or even autonomously
performing highly repetitive, precise sub-tasks (e.g., suturing
in specific layers) under human supervision. Reinforcement
Learning is being explored to train robots for complex surgical
manoeuvres in simulations.
Impact: Enhanced surgical precision, reduced invasiveness, faster patient recovery times, and expanded capabilities
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for complex procedures.
Example: A future surgical robot, powered by AI vision, might be able to automatically identify a specific blood vessel
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based on learned patterns and highlight it for the surgeon, or even autonomously complete a small, intricate stitch
perfectly, ensuring consistent quality.
Rehabilitation and Assistive Robotics
Concept: These robots assist patients in recovering from injuries or provide support for individuals with disabilities.
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New age systems are becoming more adaptable and personalised, using AI to understand and respond to individual
patient needs.
AI/ML Application: ML algorithms analyse patient movement patterns, physiological responses, and progress data
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to dynamically adjust therapy intensity or provide tailored assistance. For prosthetic limbs, AI allows for more natural
and intuitive control by interpreting muscle signals.

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Introduction to Robots: What Exactly are They?