Multi-Modal Surface Inspection Robot for 3D Flatness Mapping
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Solution Overview
Problem
Current surface inspection methods for large areas, such as sports courts, are inefficient and inaccurate, often requiring manual labor and multiple passes with fixed-resolution detection systems that fail to measure flatness and dimensionality effectively, posing safety risks and being time-consuming.
Innovation Solution
A mobile robot equipped with sensors, a communication system, and software for base motion planning, point cloud acquisition, and surface analysis, which moves in a zigzag pattern to generate 3D point clouds and high-resolution images for processing, allowing for accurate detection and marking of irregularities like bumps or depressions, enabling remote operation and efficient inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual water sprinkling inspection is used, then surface flatness can be detected, but inspection time is extremely long (2-3 hours waiting for evaporation) and efficiency is low
Solution Approach 1:
The patent replaces the mechanical water sprinkling and evaporation process with a robotic inspection system using optical sensors (cameras, depth sensors) and 3D point cloud processing. The mobile robot equipped with sensors captures surface data and generates 3D models to detect flatness issues instantly, eliminating the time-consuming water evaporation waiting period while maintaining detection accuracy.
2Device complexity
If fixed matrix test needles are used, then detection structure is simple, but spatial resolution is low (about 1 cm) and cannot measure exact depth data
Solution Approach 1:
The patent replaces the fixed matrix test needle system with a mobile robot equipped with optical cameras and depth sensors. This substitution enables continuous surface scanning with high spatial resolution and accurate depth measurement through 3D point cloud generation, while maintaining relatively simple device structure through the use of standard sensor components.
Solution Approach 2:
The patent transitions from 2D planar detection (test needles arranged in a matrix) to 3D volumetric detection using point clouds. The depth sensor and camera capture three-dimensional surface information, enabling precise measurement of depression depth and surface topology that was impossible with flat 2D needle arrays.
3Area of stationary object
If multiple personnel manually operate detection devices, then large areas can be covered, but safety risks increase due to exposure to test needles
Solution Approach 1:
The patent implements autonomous mobile robot inspection that independently navigates and scans large areas without human intervention. The robot autonomously moves across the surface, captures data, and processes information, eliminating the need for personnel to physically handle test needles or be present in hazardous areas, thereby ensuring safety while maintaining comprehensive coverage.
Solution Approach 2:
The patent replaces manual human operation of detection devices with an autonomous mobile robotic system. This substitution eliminates direct human contact with potentially hazardous test needles and equipment, allowing large areas to be inspected safely without personnel exposure to harmful factors.
4Device complexity
If fixed detection width (wheel width about 10 cm) is used, then device structure is simple, but multiple passes are required to cover large fields
Solution Approach 1:
The patent employs a mobile robot with dynamic movement capabilities that can adapt its path and speed during inspection. The robot navigates autonomously across large areas, adjusting its trajectory to efficiently cover the entire surface in fewer passes compared to fixed-width wheel systems, thereby improving productivity while maintaining manageable system complexity.
Data Source
AI summary
A system for inspecting surfaces that includes a mobile base, sensors for base navigation, sensors for surface inspection, a communication system and a host computer that executes modules for base motion planning and navigation, location, point cloud acquisition and processing, surface modelling and analysis, multi module coordination and user interfaces. The inspection procedure has the robot move in a zigzag pattern trajectory over the surface. For every fixed distance, a 3D point cloud of the surface is generated and the location of the point cloud with respect to the world coordinate system is recorded. The location of the point cloud is based on SLAM for spatial mapping. At the same time, a high-resolution photo of the corresponding area on the surface is recorded by the camera. Both the point cloud and the photo are transmitted to the host computer for processing and analysis. This information is used in a new 3D detection and image processing algorithm to find flaws in the surface like bumps or depressions. If irregular flaws are detected, the robot marks such a problematic location.