Road Marking Robot Localization for Precise On-Vehicle Painting
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Solution Overview
Problem
Current methods for applying road markings are inefficient and pose safety risks due to reliance on manual labor and lack of precision, with automated systems often being complex and ineffective in accurately localizing marking applications.
Innovation Solution
A system and method that utilize a vehicle-mounted application tool with sensors and GPS to store marking data, generate task plans, and compute joint-space trajectories for precise application of markings, incorporating sensor fusion for accurate localization and adaptive adjustments during application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual painting with stencils is used, then complex shapes and lines can be applied, but worker safety is compromised and labor costs increase
Solution Approach 1:
The patent replaces manual mechanical painting operations with an automated robotic system that uses computer vision and control algorithms. The robot autonomously navigates, localizes markings, and applies paint without human workers being exposed to traffic hazards, while maintaining the ability to create complex shapes and lines through programmable motion control.
Solution Approach 2:
The system enables the marking application process to be self-sufficient through autonomous vehicle navigation and automated robot control. The robot independently localizes target locations using computer vision, plans its motion trajectory, and executes painting operations without requiring human operators to be present in hazardous zones.
2Object-affected harmful factors
If automated marking systems are implemented, then worker safety is improved, but localization accuracy and system complexity become problematic
Solution Approach 1:
The patent introduces computer vision algorithms and fiducial marker detection as intermediary systems between the automated vehicle and the marking application process. These intermediaries enable precise localization by detecting known patterns and calculating transformation matrices, bridging the gap between vehicle position and accurate marking placement without requiring overly complex hardware.
Solution Approach 2:
The system uses fiducial markers as simplified copies or proxies for complex localization targets. These known geometric patterns serve as reference points that are easier to detect and process than natural features, enabling accurate positioning through pattern recognition and coordinate transformation without requiring sophisticated sensing systems.
3Device complexity
If hand painting with stencils is used, then equipment costs are reduced, but productivity and consistency of marking application decrease
Solution Approach 1:
The patent implements a dynamic system where the robot can adapt its motion speed, painting parameters, and trajectory in real-time based on the specific marking requirements and environmental conditions. This dynamic control enables consistent high-quality output while maintaining operational flexibility, achieving both productivity and adaptability that static manual processes cannot match.
4Manufacturing precision
If automated systems with complex algorithms are deployed, then marking precision is improved, but ease of operation by planning and field personnel deteriorates
Solution Approach 1:
The system uses fiducial markers as simplified digital proxies that bridge the gap between complex automated processing and simple user interaction. Field personnel only need to place these standardized markers, while the complex localization and positioning algorithms are automatically executed by the system, maintaining precision without increasing operational complexity.
Data Source
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AI summary
An example method includes storing marking data to specify at least one selected marking to apply at a target location along a vehicle path of travel, the marking data including a machine-readable description and a marking reference coordinate frame for the selected marking. The method also includes generating task plan data to apply the selected marking based on the marking data and at least one parameter of an application tool. The method also includes determining a location and orientation of the application tool with respect to the vehicle path of travel based on location data representing a current location of a vehicle carrying the application tool. The method also includes computing a joint-space trajectory to enable the application tool to apply the selected marking at the target location based on the task plan data and the determined location of the application tool.