Robot Task Optimization Using Historical Location Duration Patterns

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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

VSEngineering Contradiction Analysis

1Productivity

If historical task and location correlated duration data is collected and analyzed from multiple robots to optimize future tasks, then task performance and efficiency are improved, but device complexity and data processing requirements increase

Engineering Contradiction:

Improvetask performanceVSAvoiddata processing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the complex optimization problem into distinct components: data collection from multiple robots, data normalization to account for environmental variances, aggregation of historical duration data, and application of optimized task sequences. This segmentation allows each component to be processed independently, reducing overall system complexity while maintaining improved task performance.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary data processing layer that collects duration data from multiple robots, normalizes it to account for environmental differences, and generates optimized task sequences. This intermediary layer acts as a mediator between raw robot operations and performance optimization, managing the complexity of multi-robot data aggregation while delivering simplified optimization recommendations.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If tasks are optimized based on historical duration data from multiple environments, then adaptability to varying conditions is improved, but measurement precision requirements increase to account for environmental variances

Engineering Contradiction:

Improveenvironmental adaptabilityVSAvoidduration measurement precision

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent applies parameter changes by normalizing duration data to account for environmental variances. Instead of requiring precise measurements under identical conditions, the system adjusts the duration parameters based on environmental factors, allowing adaptation across diverse conditions while maintaining measurement feasibility.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent performs preliminary normalization of historical duration data before aggregation and analysis. By pre-processing the data to account for environmental differences, the system reduces the precision requirements for subsequent measurements while maintaining adaptability across varying conditions.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If systematic issues are detected through aggregated historical data from multiple robots, then reliability of task execution is improved, but loss of time for data aggregation and analysis increases

Engineering Contradiction:

Improvetask execution reliabilityVSAvoiddata aggregation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent performs preliminary aggregation and normalization of duration data from multiple robots in advance, creating a repository of historical performance data. This preliminary action allows systematic issues to be detected more quickly when needed, improving reliability without requiring extensive real-time data collection.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements partial data aggregation by focusing on specific task types and environmental conditions that are most relevant to detecting systematic issues. Rather than aggregating all possible data, the system selectively processes data that provides the most value for reliability improvement, reducing time loss while maintaining detection effectiveness.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11235464B1Robot task optimization based on historical task and location correlated durations

Publication Date: 2022.02.01 GDM HOLDING LLC

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.