Medical robotic arm

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Overview
Multi‑arm robot deployments face high‑dimensional control complexity, limited availability of large‑scale training data, challenging dynamics modeling and constraints of classical control methods. These factors impact safety, coordination efficiency and adaptability in industrial multi‑robot or human‑robot collaborative environments.

System modules

• Robot collision avoidance
  • trajectory prediction
  • dynamic path adjustment
  • digital modeling of the work environment
• Robot teach‑in training
  • high‑precision positioning robotic arms
  • user‑friendly data collection and training platform
• Model Predictive Control (MPC)
  • trade‑off management between multi‑arm efficiency, energy use and safety
  • dynamic constraint handling (velocity, acceleration, etc.)
  • MPC strategy simulation and validation

Technical advantages

• Efficient teach‑in data acquisition: attach markers to operators to capture motion sequences as training data for deep learning models, enabling robots to learn and reproduce human actions.
• Collision avoidance for multi‑arm systems: machine learning analyzes past collision records to optimize avoidance strategies and improve operational safety.
• Predictive task allocation: historical data‑driven forecasting of task demand and priority to adjust allocations before failures occur.
• Adaptation to complex environments: deep learning enables environment understanding, obstacle recognition and dynamic behavior handling for precise task assignment.

Related products

• 瑞瞳®PRO — multi‑source vision scanning and positioning system
• 瑞瞳®MAX‑Vision — integrated laser positioning RGB stereo camera
• 瑞瞳®LITE — cost‑effective stereo vision camera
• 瑞瞳®MAX — high‑accuracy medium‑to‑long‑range stereo vision camera

Technical specifications

• Brand: 艾瑞迈迪 (Ariemedi)
• Solution name: Multi‑arm collaboration and human‑machine interaction solution
• Core capabilities: multi‑arm collision avoidance, robot teach‑in training, Model Predictive Control (MPC), predictive task allocation, complex environment adaptation
• System modules: robot collision avoidance; robot teach‑in training; Model Predictive Control (MPC)
• Technical highlights: trajectory prediction, dynamic path adjustment, digital environment modeling, high‑precision positioning, deep learning data acquisition and utilization, dynamic constraint management, simulation validation
• Applicable scenarios: multi‑robot coordinated workcells, industrial or medical robot collaboration requiring high safety and efficient scheduling
• Related products/accessories: 瑞瞳® series vision and positioning devices for perception and localization