Robot 3D Point-Cloud Localization for Confined-Space Navigation
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Solution Overview
Problem
Manual control of robots in confined or hazardous environments is prone to human error and uncertainty due to multiple degrees of freedom and non-linear joints, which can lead to operational inefficiencies and potential damage.
Innovation Solution
A method involving 3D scanning to create a point cloud of the environment, comparing it to a virtual 3D model to determine the robot's position, and calculating a movement trajectory for precise navigation and maintenance operations, potentially using multiple 3D scanners and support robots for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual control is used to operate the robot, then the operator can control the robot to navigate and perform tasks, but human error and uncertainty increase, reducing reliability
Solution Approach 1:
The robot performs self-localization by comparing 3D point cloud data from its environment sensor with the virtual 3D model, automatically determining its own position and orientation without continuous manual intervention. This self-service capability reduces human error while maintaining operational control
Solution Approach 2:
The patent replaces manual mechanical control with an automated control system that uses 3D environmental mapping and comparison algorithms. The control system automatically calculates the robot's pose and generates navigation commands, substituting human operator actions with computational processes that eliminate human error
2Adaptability or versatility
If multiple degrees of freedom and non-linear joints are provided for articulation, then the robot can perform complex tasks, but uncertainty regarding robot displacement increases, reducing manufacturing precision
Solution Approach 1:
The system continuously captures 3D point cloud data from the environment sensor and compares it with the virtual 3D model to provide real-time feedback on the robot's actual position and orientation. This feedback loop compensates for uncertainties introduced by multiple degrees of freedom and non-linear joints, maintaining precise positioning despite complex articulation
Solution Approach 2:
The patent transitions from relying solely on internal robot state information to using external 3D environmental mapping for position determination. By comparing the observed 3D point cloud with the virtual model, the system determines position in three-dimensional space, adding an external reference dimension that compensates for internal articulation uncertainties
3Reliability
If automated control is implemented using 3D scanning and virtual model comparison, then human error is reduced and reliability improves, but device complexity increases
Solution Approach 1:
The virtual 3D model serves multiple functions: it represents the environment for localization, provides a reference for position determination, and enables trajectory calculation. This multi-functionality reduces the need for separate systems, managing complexity while maintaining high reliability through integrated automated control
Data Source
AI summary
There is provided a method of controlling a robot within an environment comprising: i) receiving, from a 3D scanner, data relating to at least a portion of the environment for constructing a 3D point cloud representing at least a portion of the environment; ii) comparing the 3D point cloud to a virtual 3D model of the environment and, based upon the comparison, determining a position of the robot; then iii) determining a movement trajectory for the robot based upon the determined position of the robot. Also provided is a control apparatus and a robot control system.