Mobile Robot Safety Control Using AI-Based Hazard Sensing
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Solution Overview
Problem
Existing mobile robot devices lack a reliable safety mechanism to prevent accidents while providing services, as they cannot effectively sense and respond to their surroundings during operation.
Innovation Solution
A mobile robot device equipped with a sensing unit and a processor that uses a training model trained with an artificial intelligence algorithm to adjust its safety operation level based on sensed environmental information, controlling its movement and arm device operations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a mobile robot device travels to provide services, then service delivery capability is improved, but the risk of accidents due to malfunction increases
Solution Approach 1:
The system performs preliminary sensing of the surrounding environment before executing movement or arm device operations. The processor obtains sensing information from sensors about the environment, predicts potential hazards using a trained prediction model, and adjusts safety operation levels in advance before accidents can occur, enabling proactive safety management
Solution Approach 2:
The system continuously obtains sensing information from sensors during operation, feeds this information back to the prediction model, and dynamically adjusts safety operation levels based on real-time environmental conditions. This closed-loop feedback mechanism allows the robot to adapt its safety measures according to actual surrounding conditions
2Reliability
If the robot dynamically adjusts safety operation level based on environment, then safety is improved, but device complexity increases
Solution Approach 1:
The system uses a prediction model that is trained using artificial intelligence algorithms to automatically analyze sensing information and determine appropriate safety operation levels without requiring complex manual control systems. The trained model performs the complex decision-making autonomously, reducing the need for elaborate control logic
Solution Approach 2:
The system adjusts safety operation levels by changing operational parameters such as movement speed, arm device movement speed, and notification settings based on predicted hazard levels. Instead of complex structural changes, the system achieves adaptability through dynamic parameter adjustment controlled by the AI prediction model
Data Source
AI summary
Provided are an artificial intelligence (AI) system utilizing a machine learning algorithm such as deep learning and an application thereof. A method of providing, by a mobile robot device including an arm device, a service to a user includes obtaining sensing information obtained by sensing a surrounding environment of the mobile robot device while the mobile robot device is traveling, changing, based on the sensing information which has been obtained by sensing, a safety operation level of the mobile robot device, and controlling, based on the changed safety operation level, an operation of the mobile robot device, wherein the safety operation level is a level for controlling an operation related to a movement of the mobile robot device and a motion of the arm device, and the mobile robot device changes the safety operation level by applying the obtained sensed information to a training model trained using an artificial intelligence algorithm.