Mobile Robot Mission Scheduling Using Geofencing and User Behavior
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Solution Overview
Problem
Conventional mobile robot scheduling is cumbersome and lacks adaptability, relying on static time-based schedules that fail to account for user behavior and contextual information, leading to inefficiencies and increased manual intervention.
Innovation Solution
Implementing a system that uses geofencing and contextual information to dynamically schedule and control mobile robots, allowing for mission routines based on user location, behavior, and experience, enabling automatic adjustments and enhanced user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static time-based schedules are used for robot missions, then scheduling simplicity is maintained, but adaptability to user behavior and contextual information deteriorates
Solution Approach 1:
The patent transforms static time-based schedules into dynamic schedules that automatically adjust based on real-time user behavior and contextual information. The system monitors user location, activity patterns, and environmental conditions to dynamically modify mission timing, duration, and execution parameters, enabling the robot to adapt to changing user needs without manual intervention.
Solution Approach 2:
The system implements continuous feedback loops where user behavior data, location information, and contextual parameters are constantly monitored and fed back to the scheduling algorithm. This feedback mechanism enables the system to learn from user interactions and automatically refine mission schedules, improving adaptability while maintaining automated operation.
2Ease of operation
If manual scheduling intervention is increased, then mission customization improves, but user convenience and automation level deteriorate
Solution Approach 1:
The patent enables the scheduling system to serve itself by automatically monitoring user behavior patterns, analyzing contextual information, and generating optimized mission schedules without requiring manual user input. The system learns user preferences over time and autonomously adjusts scheduling parameters, providing both high automation and personalized customization.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns and contextual factors in advance to pre-calculate optimal mission schedules. By anticipating user needs based on historical data and current context, the system prepares schedules ahead of time, reducing the need for real-time manual adjustments while maintaining high convenience levels.
3Productivity
If geofencing and contextual monitoring are implemented, then mission adaptability improves, but system complexity and computational requirements worsen
Solution Approach 1:
The patent divides the complex scheduling system into modular functional components: geofencing module, contextual information collector, behavior pattern analyzer, and mission scheduler. Each module handles specific tasks independently, reducing overall system complexity while enabling comprehensive adaptability. The segmented architecture allows for easier maintenance and scalability.
Data Source
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AI summary
Described herein are systems, devices, and methods for scheduling and controlling a mobile robot based on user location, user behavior, or other contextual information. In an example, a mobile cleaning robot comprises a drive system configured to move the mobile cleaning robot about an environment in a user's residence, and a controller circuit configured to receive an indication of a user entering or exiting a pre-defined geographical zone with respect to a location of the user's residence. Such indication may be detected using location and geofencing services of a mobile device. Based on the indication of the user entering or exiting the geofence, the controller circuit may generate a motion control signal to navigate the mobile cleaning robot to conduct a mission in the environment.