Mobile Robot Scheduling Using Semantic Annotation and User Experience
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Solution Overview
Problem
Conventional mobile robot scheduling and control systems lack personalization and efficiency, relying on generic map-and-location approaches that fail to incorporate user preferences, contextual information, and user experience, leading to repetitive and error-prone mission creation and reduced user experience.
Innovation Solution
A mobile robot system that uses semantically annotated objects and user experience-based mission routines, allowing users to create and manage personalized cleaning missions with spatial and contextual information, enabling intuitive communication and enhanced robot behavior through a user-friendly interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic map-and-location approaches are used for robot scheduling, then the system is simple to implement, but the system lacks personalization and user experience integration
Solution Approach 1:
The system performs preliminary actions by automatically generating mission routines based on user profiles and contextual information before user intervention. User preferences, spatial relationships, and behavioral patterns are pre-processed to create personalized mission templates, reducing the need for repetitive manual mission creation while maintaining simplicity.
Solution Approach 2:
The robot system serves itself by autonomously learning user preferences and contextual patterns from observed behavior and feedback. The system automatically adjusts mission routines based on accumulated experience without requiring explicit reprogramming, enabling personalization while keeping the interface simple for users.
2Productivity
If manual mission creation is required for each cleaning task, then the system maintains precise control, but the process becomes repetitive and error-prone
Solution Approach 1:
The system creates copies of successful mission routines and reuses them with automatic adaptations. Instead of manually creating each mission from scratch, the system replicates and modifies existing mission templates based on current contextual information and user preferences, significantly improving efficiency while maintaining control precision.
Solution Approach 2:
Mission routines are pre-configured based on historical data and user patterns before execution is needed. The system prepares multiple mission templates in advance, allowing rapid deployment without repetitive manual creation, thereby reducing user burden and errors while maintaining precise control over cleaning parameters.
3Reliability
If the system incorporates contextual information and user experience, then the robot becomes more intelligent and personalized, but the data processing complexity increases
Solution Approach 1:
The system segments contextual information and user experience data into distinct, manageable categories such as spatial relationships, temporal patterns, user preferences, and environmental conditions. Each segment is processed independently by specialized modules, improving reliability through focused analysis while reducing overall data processing complexity through modular architecture.
Solution Approach 2:
An intermediary processing layer is introduced between raw data collection and mission execution. This intermediary layer aggregates, filters, and standardizes contextual information and user experience data before feeding it to the mission planning system, reducing data processing complexity while maintaining high reliability through controlled information flow.
4Loss of information
If semantically annotated objects are used instead of simple locations, then the system provides better contextual understanding, but the mapping and annotation process becomes more complex
Solution Approach 1:
The system merges semantic annotations with the existing map structure by integrating object attributes, spatial relationships, and contextual information directly into the location data. This combination preserves rich contextual information without requiring separate annotation systems, reducing complexity through unified data representation while maintaining comprehensive contextual understanding.
Data Source
AI summary
Described herein are systems, devices, and methods for scheduling and controlling a mobile robot using a textual and user experienced-based mission routine. In an example, a mobile cleaning robot comprises a drive system to move the mobile cleaning robot about an environment, a sensor circuit to detect an object in the environment, and a controller circuit to receive a mission routine including data representing an editable schedule including at least one of time or order for performing one or more cleaning tasks with respect to a semantically annotated object that include spatial or contextual information of the detected object, or with respect to user experience or user behavior. The controller circuit navigates the mobile cleaning robot to conduct a mission in accordance with the mission routine.