Mobile Robot Task Adaptation Using User Habit Learning
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Solution Overview
Problem
Mobile robots face inefficiencies in task execution due to mismatched user preferences and pre-defined task functions, leading to wasted user time and resources.
Innovation Solution
A task optimization method and device that uses machine learning to analyze user usage habits and real-time data to optimize task execution in mobile robots, adjusting tasks based on usage patterns and probabilities to provide a more efficient and intelligent user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developer-provided pre-defined task functions are used, then the mobile robot can execute tasks with fixed functionality, but the task execution efficiency deteriorates due to mismatch with user preferences
Solution Approach 1:
The patent transforms static pre-defined task functions into dynamic adaptive tasks by continuously learning user usage habits through machine learning algorithms. The system adjusts task parameters, execution sequences, and resource allocation in real-time based on accumulated usage data, enabling the robot to adapt its behavior to match user preferences while maintaining reliable task completion
Solution Approach 2:
The mobile robot performs self-optimization by automatically analyzing its own usage patterns and user interaction data. Through built-in machine learning models, the system independently identifies efficient task execution patterns and adjusts its operational parameters without requiring external reprogramming or manual configuration, thereby improving productivity while maintaining reliability
2Reliability
If developer-provided pre-defined task functions are used, then the mobile robot can execute tasks with fixed functionality, but user time consumption increases due to inability to adapt to user habits
Solution Approach 1:
The system performs preliminary learning of user preferences by analyzing usage patterns before actual task execution. Machine learning models continuously process historical data to predict user intentions and pre-adjust task parameters, reducing the time users need to spend on manual configuration and speeding up task completion while ensuring reliable execution
3Reliability
If developer-provided pre-defined task functions are used, then the mobile robot can execute tasks with fixed functionality, but resource waste occurs due to mismatch with actual user needs
Solution Approach 1:
The patent dynamically adjusts task execution parameters such as resource allocation, processing priority, and operational intensity based on learned user preferences and real-time context. By changing these parameters adaptively, the system optimizes resource utilization to match actual user needs while maintaining stable and reliable task execution
Data Source
AI summary
A task optimization method and a task optimization device in a mobile robot are provided. The task optimization method includes: obtaining at least one task type in a mobile robot and usage information when all users use a task corresponding to each task type; separately performing machine learning on the usage information of all the users corresponding to each task type to obtain at least one piece of user's usage habit information corresponding to each task type and usage probability thereof, thereby performing machine learning on usage information when all users use the task corresponding to the task type; based on the at least one piece of usage habit information corresponding to each task type, the usage probability thereof and the real-time usage information, optimizing the task corresponding to the task type used by the user in real time.