Road Marking Trajectory Planning for Precise Vehicle-Mounted Application
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Solution Overview
Problem
Existing automated systems for applying road markings face challenges in accurately localizing marking placement and are often too complex for practical use, leading to inefficiencies and safety risks for workers.
Innovation Solution
A method and system that utilize a vehicle-mounted application tool with sensors and GPS to determine the vehicle's pose and compute a joint-space trajectory for precise marking application, incorporating sensor fusion to enhance localization accuracy and adapt to on-the-fly changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used to apply road markings, then productivity and safety are improved, but device complexity and localization accuracy worsen
Solution Approach 1:
The system divides the marking application process into distinct phases: path surveying with fiducial detection, real-time pose estimation using GPS and sensor fusion, and execution phase with automated tool control. This segmentation allows each subsystem to be optimized independently while maintaining overall system manageability and reduced complexity.
Solution Approach 2:
The system employs self-contained application tools mounted on the vehicle that autonomously apply markings based on pre-computed trajectories and real-time pose feedback, eliminating the need for external guidance systems or complex human-machine coordination interfaces.
2Measurement precision
If sensor fusion and GPS are used to determine vehicle pose, then localization precision is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary path surveying and fiducial detection before the actual marking application. During this survey phase, the optimal path and fiducial locations are pre-identified and stored, enabling simplified real-time pose estimation during execution without requiring complex real-time processing of all sensor data.
Solution Approach 2:
Fiducials serve as intermediary reference objects that simplify the localization problem. By detecting these known fiducial markers in the environment and comparing their observed positions with pre-stored survey data, the system can accurately determine vehicle pose without directly processing complex raw sensor data from multiple sources.
3Adaptability or versatility
If hand painting with stencils is used, then adaptability to complex shapes is improved, but safety and productivity worsen
Solution Approach 1:
The system uses dynamic trajectory computation that adapts to complex marking geometries by calculating appropriate application tool orientations and positions along the path. The joint-space trajectory computation allows the rigid application tool to dynamically adjust its pose to match complex marking shapes, achieving the adaptability previously requiring manual stencil work.
4Device complexity
If intermittent or sustained issues with localization accuracy occur, then marking precision worsens, but system simplicity is maintained
Solution Approach 1:
The system implements continuous feedback by repeatedly detecting fiducials during vehicle traversal and comparing observed fiducial positions with pre-stored survey data. This feedback loop enables real-time pose correction and compensation for drift or environmental changes, maintaining marking precision without requiring overly complex systems.
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.