Driving mechanism development for autonomous hospital bed

A growing concern in the healthcare industry is the ageing population it is serving. Demand is foreseen to spike and a range of healthcare services that range from acute to community care is necessary for a holistic healthcare system. Capacity of the healthcare service workforce is however limited a...

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Bibliographic Details
Main Author: Tan, Jia Liang
Other Authors: Li King Ho Holden
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/141606
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Institution: Nanyang Technological University
Language: English
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Summary:A growing concern in the healthcare industry is the ageing population it is serving. Demand is foreseen to spike and a range of healthcare services that range from acute to community care is necessary for a holistic healthcare system. Capacity of the healthcare service workforce is however limited and technological means have been explored to increase manpower efficiency. An autonomous hospital bed is thus put forth to reduce the manpower required in non-emergency bed transportation. This is in line with the strategic thrust of diversifying the healthcare landscape in response to the ageing population. Time can be freed up for training opportunities, allowing healthcare workers to transit from hospital to community or home-based service providers. Development of the bed is multi-faceted and this project continues from a previous iteration, focusing on the development of the drivetrain, its control and the integration of an object-detection based computer vision approach for autonomous navigation. Introduction of a new drivetrain, the active split offset caster (ASOC) has shown how it can help reduce learning curve for users compared to the previous drivetrain. The project has also demonstrated effective kinematic control of the ASOC module on a realistic hospital bed aspect ratio. Robotics Operating System (ROS) was also employed to develop the autonomous control system using an object detection neural network. The usage of sensors to negotiate turns autonomously was also explored in the project. Overall, the project has helped to drive developmental efforts in mechanical design and autonomous control aspects of the final product.