Autonomous Robot Navigation With Digital Map Anomaly Correction
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Solution Overview
Problem
Existing systems lack the ability to accurately and efficiently perform operations within enclosed or dangerous spaces, such as cutting pipes, due to reliance on manual observation and lack of automated detection methods that can correlate disjointed data sets.
Innovation Solution
The use of a robotic system equipped with multiple sensors, including cameras, infrared recognizers, LIDAR, and motion sensors, to create a coherent 3D representation of the environment through sensor fusion. This system employs artificial intelligence and machine learning algorithms to predict and mark feature locations, enabling automated scanning, mapping, and operational processes like cutting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation methods are used to detect features in enclosed spaces, then the system complexity is low, but the detection accuracy and efficiency are insufficient
Solution Approach 1:
The patent combines multiple sensors (cameras, LIDAR, infrared sensors, gas detectors) into an integrated robotic inspection system. This merging of sensors allows the system to collect and correlate multiple types of data simultaneously, significantly improving detection accuracy while managing complexity through integrated processing
Solution Approach 2:
The patent introduces AI/ML algorithms as intermediaries that process and correlate data from multiple sensors. These algorithms act as mediators that transform raw sensor data into accurate feature detections, enabling high measurement precision without requiring direct manual observation
2Productivity
If automated detection systems are implemented, then detection efficiency improves, but the ability to correlate disjointed data sets remains insufficient
Solution Approach 1:
The system merges data from multiple sensors (visual, thermal, gas composition, LIDAR) into a unified dataset. This combination allows the AI algorithms to correlate information that would otherwise remain disjointed, maintaining complete information while improving detection efficiency
Solution Approach 2:
The patent implements feedback loops where the system continuously processes sensor data, compares it with previously collected data, and refines its detections. This feedback mechanism ensures that no information is lost while maintaining high detection efficiency through iterative improvement
3Adaptability or versatility
If human operators are deployed in hazardous environments, then operational flexibility is maintained, but safety risks increase
Solution Approach 1:
The robotic system performs inspection and operational tasks autonomously without requiring human presence in hazardous environments. The system navigates, detects features, and executes operations independently, eliminating safety risks to human operators while maintaining operational flexibility
Solution Approach 2:
The patent replaces human operators with an automated robotic system equipped with sensors and AI processing. This substitution eliminates the harmful exposure of humans to hazardous conditions while preserving adaptability through programmable autonomous decision-making
4Measurement precision
If multiple sensors are integrated for comprehensive data collection, then measurement accuracy improves, but device complexity increases
Solution Approach 1:
The robotic system is designed with multi-functional sensors that can detect multiple types of features (visual defects, thermal anomalies, gas leaks, dimensional variations) simultaneously. This universality allows comprehensive data collection without proportionally increasing system complexity
Solution Approach 2:
AI/ML algorithms serve as intermediaries that process and integrate data from multiple sensors. These algorithms manage the complexity of sensor integration by automatically correlating data from different sources, enabling high measurement precision without linear complexity increases
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves high accuracy and efficiency in detecting features and performing operations within enclosed spaces, reducing the risk of human error and enhancing safety by allowing operations in hazardous environments.
Implementation Method 1
a visual camera, an infrared camera, a spatial distance sensor (e.g., LIDAR)
Implementation Method 2
a visual camera, an infrared camera, a spatial distance sensor (e.g., LIDAR)
Implementation Method 3
Other systems utilize infrared cameras to detect temperature variations within the scanned environment
Data Source
AI summary
A method includes creating a digital map of an environment, loading the digital map on a moveable robot, wherein the robot is placed in the environment, generating a trajectory path plan from a current position to a desired position using the digital map, the trajectory path having a plurality of waypoints, causing the robot to traverse within the environment in accordance with the trajectory path plan, collecting sensor data in real time while the robot is traversing within the environment, detecting, based on the collecting step, at each waypoint, whether an anomaly is present between an existing waypoint and a subsequent waypoint, and performing a corrective action of the robot based on the detecting step.