Robot Reference Frame Adjustment for Multi-Sensor 3D Mapping
Find Innovative SolutionsGenerate Solutions
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, which fuse data to create a coherent 3D representation of the environment, enabling automated detection and operation within these spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual observation and conventional detection methods are used, then human intervention is required for detecting features and defects, but the process is susceptible to errors and lacks efficiency
Solution Approach 1:
The system employs automated detection algorithms that process sensor data without human intervention. The processor automatically identifies features and defects by analyzing patterns in the collected data, enabling the system to serve itself in the detection process while maintaining high accuracy and efficiency
Solution Approach 2:
Manual observation is replaced with automated sensor-based detection systems. The mechanical process of human visual inspection is substituted with electronic sensors and computational algorithms that continuously scan and analyze the environment, eliminating human error and increasing detection speed
2Loss of information
If multiple sensors are used to detect features and temperature variations, then more data is collected, but the data sets remain disjointed and cannot be correlated
Solution Approach 1:
The system merges data from multiple sensors including visual cameras and infrared cameras into a unified dataset. The processor correlates these previously disjointed data streams by synchronizing them in time and space, creating a comprehensive view of the environment that retains all information while eliminating data silos
Solution Approach 2:
A data fusion module acts as an intermediary between multiple sensors and the processing system. This intermediary component standardizes and correlates data from different sensor types, transforming disjointed datasets into a coherent information structure that can be efficiently analyzed
3Productivity
If automated detection systems are implemented, then efficiency is improved, but the system complexity increases
Solution Approach 1:
The robotic system is designed with multi-functional capabilities, integrating detection, navigation, and operation functions into a single platform. This universal system performs multiple tasks using shared hardware and software resources, improving operational efficiency while avoiding the complexity of multiple separate systems
Solution Approach 2:
The system employs dynamic resource allocation where computational resources and sensor activation are adjusted based on operational needs. During detection phases, full sensor arrays are activated, while during navigation, only essential sensors operate, optimizing performance while managing system complexity through adaptive behavior
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
This approach allows for precise and efficient execution of operations, such as cutting, within enclosed spaces, reducing the risk of human error and improving the accuracy of digital mapping and feature detection.
Implementation Method 1
a LIDAR camera 24 configured to provide a three-dimensional point cloud
Implementation Method 2
Other systems utilize infrared cameras to detect temperature variations within the scanned environment
Data Source
AI summary
A method for controlling movement of a robot in an environment, wherein the robot has a plurality of sensors associated therewith. includes determining an initial scan frame of reference for the robot, wherein scan frame of reference is calculated based on an initial point of origin, calculating a new point of origin based on a reading from at least one of the sensors, and adjusting the initial scan frame of reference to determine a new scan frame of reference based on the new point of origin, wherein the adjusting step is based on the difference between the initial scan frame of reference and the new scan frame of reference