Robot Task Scheduling Using Historical Location Duration Data
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Solution Overview
Problem
Assessing and optimizing the performance of autonomous or semi-autonomous robots in variable environments is challenging due to the non-repeatability of tasks, such as in multipurpose residential cleaning robots, where tasks differ each time due to varying obstructions and cleanliness levels.
Innovation Solution
Collecting and analyzing historical task and location correlated duration data from robots to optimize future tasks, including aggregating data across multiple robots and environments, detecting systemic issues, reengineering tasks, generating user notifications, and creating schedules that accommodate user restrictions and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If historical task and location correlated duration data is collected and analyzed from multiple robots to optimize future tasks, then task performance and adaptability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex data processing task into distinct functional modules: data collection from multiple robots, data normalization to account for environmental variances, aggregation across robots and locations, analysis to detect systemic issues, and generation of optimized task sequences. This modular segmentation manages system complexity by organizing the data processing pipeline into manageable, independent components that can be developed and maintained separately.
Solution Approach 2:
The patent introduces a centralized data processing system that acts as an intermediary between multiple robots and the task optimization function. This intermediary collects duration data from various robots, normalizes it to account for environmental differences, aggregates it across multiple locations and robots, and generates optimized task sequences. The intermediary absorbs the complexity of data processing while presenting a simple interface for task optimization.
2Productivity
If task optimization is performed based on aggregated historical data from multiple robots and environments, then productivity and efficiency are improved, but measurement precision and data normalization requirements increase
Solution Approach 1:
The patent applies local quality by normalizing duration data according to specific environmental characteristics and robot types. Instead of treating all duration data uniformly, the system adjusts and weights data based on local conditions such as environment type, robot model, and task specifics. This ensures that duration measurements are accurately comparable across different contexts while preserving the productivity benefits of aggregated historical data.
Solution Approach 2:
The patent changes parameters of the duration data through normalization processes that adjust for environmental variances, robot differences, and task variations. By transforming raw duration measurements into normalized values that account for these parameter differences, the system maintains measurement precision while enabling meaningful aggregation across diverse robots and environments to improve productivity.
3Reliability
If systemic issues are detected and tasks are reengineered based on aggregated historical duration data, then reliability and task completion quality are improved, but device complexity and processing time increase
Solution Approach 1:
The patent performs preliminary analysis of historical duration data to detect systemic issues and identify optimization opportunities before executing task reengineering. By analyzing aggregated data from multiple robots and locations in advance, the system proactively identifies patterns and systemic problems that can be addressed through task reengineering, rather than reacting to failures after they occur. This preliminary action improves reliability while managing processing time through focused, data-driven insights.
Solution Approach 2:
The patent implements a feedback mechanism where aggregated historical duration data from multiple robots is continuously analyzed to detect systemic issues, and the insights are fed back into task reengineering processes. This feedback loop enables the system to progressively improve task reliability by learning from historical performance data across the robot fleet, while processing time is managed through efficient aggregation and analysis of the feedback data.
Data Source
AI summary
Methods, apparatus, systems, and computer-readable media are provided for optimizing robot-implemented tasks based at least in part on historical task and location correlated duration data collected from one or more robots. Historical task and location correlated duration data may, in some implementations, include durations of different tasks performed in different locations by one or more robots in one or more particular environments, and knowledge of such durations may be used to optimize tasks performed by the same or different robots in the future.