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Smart Robotic Nursing Assistant

Healthcare Robotics

A smart platform that trains robots to learn and safely perform everyday nursing tasks by watching humans—seamlessly working alongside staff in busy hospital settings.
Vector (2)-da6325

Institute:
Institute of Information Technology, Mandi

Vector (3)-42d8ac

PI Name:
Prof. Laxmidhar Behera

Technology Readiness Level (TRL)
4

Problem
Addressed

Hospitals face increasing demands, particularly during pandemic-like crises, requiring innovative solutions to alleviate the burden on healthcare workers. Current systems lack the adaptability and efficiency needed to learn and execute diverse tasks dynamically. To address this, we developed a novel learning framework enabling mobile robot manipulators (robotic nursing assistants) to learn tasks from video demonstrations and execute them interactively
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About the
Technology

  • This technology creates and critically evaluates a general-purpose architecture for learning different routine tasks that can assist nurses in the hospitals.
  • Represent the human (nurse) demonstrations in a scalable and alternate relational space, that will allow us to transfer the task to heterogeneous robots robustly.
  • Exploit offline computation to achieve fast, robust and collision-free motion planning in a dynamically changing environment.
  • Software that will allow us to learn, and transfer these skills across various robotic platforms, minimizing the platform-specific setup
  • Robot is able to learn new task and execute them as per given input command.
  • Integrated multi-modal input command system that accepts voice commands, gesture recognition, and touch screen based input.
  • Autonomous navigation in dynamic environment (eg. obstacle avoidance).

Application Areas & Use Cases

  • Application areas: hospitals and clinics, elderly care facilities, home-based care, rehabilitation centers, tele-medicine and remote monitoring, pandemic crisis such as COVID-19 and infection control
  • Use cases: patient monitoring and data collection, Medication Management, emotional Support and Companionship, Assistance in Rehabilitation Therapy, emergency response with the mobile platform for tele-medicine consultations.
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Weight and Battery life 30-35 kg, typically 2 hrs
Mobility System Wheels for movement. Camera System: High-definition RGB cameras for visual monitoring and LiDAR for navigation and obstacle detection
Manipulation Capabilities Single robotic arm (7-DoF) with 2-finger gripper
Navigation and Mobility Indoor autonomous navigation, anti-collision technology, and adjustable speed
Lift Capability by Robotic Arm Dry electrode based headset and wireless model
Communication and User Interface Maximum 5K g
Communication and User Interface Touchscreen displays for manual controls, Voice interaction via microphones and speakers
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