Robot Anomaly Classification for Task Path Obstacle Resolution
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Solution Overview
Problem
Existing robots struggle to effectively detect and resolve anomalies in their environment, such as objects or conditions that deviate from the norm, which can hinder their performance and efficiency in tasks like cleaning and delivery.
Innovation Solution
Implementing machine learning models on robots to classify objects and environments as anomalies or non-anomalies using historical data, allowing the robots to respond appropriately by moving the object, alerting users, or adjusting their tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robots use traditional detection methods without machine learning, then device complexity is low, but anomaly detection precision is insufficient
Solution Approach 1:
The patent replaces traditional mechanical detection systems with machine learning-based detection systems. The robot uses machine learning models trained on historical data to automatically classify objects as anomalies or non-anomalies, substituting complex mechanical detection mechanisms with intelligent software-based classification that achieves higher precision without proportionally increasing hardware complexity.
Solution Approach 2:
The patent changes the detection parameters from simple sensor-based mechanical detection to multi-parameter machine learning analysis. By training models on historical data with various object characteristics, the system can detect anomalies based on multiple parameters simultaneously, significantly improving detection precision while the complexity increase is managed through software optimization.
2Reliability
If robots classify and respond to all objects, then task completion accuracy improves, but productivity decreases due to increased processing time
Solution Approach 1:
The patent applies partial action by classifying only those objects that are truly anomalous based on machine learning analysis, rather than processing every object with equal detail. The system uses historical data to identify patterns and focuses computational resources only on objects that deviate from normal patterns, maintaining high accuracy while improving overall productivity by avoiding unnecessary processing of routine objects.
Solution Approach 2:
The patent implements feedback mechanisms where the robot learns from historical data and previous classifications. The machine learning model continuously improves its anomaly detection accuracy by processing feedback from past detections, allowing the system to become more efficient over time. This feedback loop enables the robot to maintain high task completion accuracy while progressively improving productivity as the model becomes more refined.
3Object-affected harmful factors
If robots navigate around all detected objects, then safety improves, but loss of time increases due to frequent task interruptions
Solution Approach 1:
The patent applies preliminary action by pre-classifying objects as anomalies or non-anomalies using machine learning models before the robot encounters them during task execution. By analyzing historical data and predicting which objects require avoidance, the system prepares navigation decisions in advance, ensuring safety while minimizing actual task interruptions. The robot only deviates from its path when truly necessary, based on pre-processed anomaly classification.
Data Source
AI summary
Methods, apparatuses, and systems associated with anomaly detection and resolution are described. Examples can include detecting, via a sensor of a robot, an object in a path of the robot while the robot is performing a task in an environment and classifying the object as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a machine learning model. Examples can include proceeding with the task responsive to classification of the object as a non-anomaly and the environment as non-anomalous and resolving the anomaly or the anomalous environment and proceeding with the task responsive to classification of the object as an anomaly or the environment as anomalous.