Autonomous Pavement Crack Sealing Robot
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Solution Overview
Problem
Current methods for identifying and filling cracks in pavement are manual, time-consuming, and pose safety risks due to high-speed traffic and daylight dependency, leading to inconsistent results and inefficiencies.
Innovation Solution
A system comprising cameras and a processor that autonomously identifies cracks and programs a robot to fill them using a nozzle with a puck width, allowing for efficient crack filling during day or night without obstructing traffic, by obtaining images, identifying crack regions, generating filling paths, determining sealant volume, and sending instructions to the robot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual crack filling is performed during the day, then workers can see the pavement clearly, but it is time-consuming and poses safety risks
Solution Approach 1:
The patent replaces manual mechanical crack filling operations with an autonomous robotic system equipped with imaging sensors, processors, and automated sealing mechanisms. The robot uses camera-based detection to identify cracks and automatically applies sealant without human intervention, eliminating safety risks to workers while maintaining high productivity through continuous operation capability.
Solution Approach 2:
The robotic system performs self-directed crack filling operations by autonomously navigating, detecting cracks through its own sensors, planning paths, and applying sealant. The system serves itself by integrating detection, decision-making, and execution functions into a single autonomous unit that operates independently without requiring human workers present at the worksite.
2Loss of time
If manual crack filling is performed, then workers can identify and fill cracks, but it requires daylight and is time-consuming
Solution Approach 1:
The robotic system enables crack filling operations to be performed during off-peak nighttime hours when traffic is lighter, rather than during daytime when manual workers operate. This periodic scheduling of operations during less busy times reduces traffic disruption while the robot's autonomous capabilities allow it to work efficiently during these non-traditional hours without requiring daylight.
Solution Approach 2:
The substitution of manual detection with automated imaging sensors and computer vision algorithms eliminates the need for daylight, allowing the system to detect and fill cracks during nighttime operations. The robotic system processes images and identifies cracks using electronic sensors and computational methods rather than human visual inspection, enabling flexible operating hours.
3Manufacturing precision
If a robot fills cracks autonomously, then safety risks are reduced and consistency is improved, but the system complexity increases
Solution Approach 1:
The robotic system integrates multiple functions including crack detection through imaging sensors, path planning algorithms, sealant delivery mechanisms, and navigation capabilities into a single multi-functional platform. This universal design consolidates what would otherwise require separate systems, managing complexity while achieving consistent, high-precision crack filling through coordinated operation of integrated components.
Solution Approach 2:
The system uses real-time feedback from imaging sensors to detect crack locations, adjust the robot's path, and monitor sealant application quality. The processor continuously analyzes sensor data to make adjustments during operation, ensuring consistent filling results. This closed-loop feedback control compensates for system complexity by using information processing to maintain precision.
4Productivity
If crack filling is performed during the day, then visibility is sufficient, but traffic obstruction increases
Solution Approach 1:
The robotic system schedules crack filling operations during nighttime or off-peak hours when traffic volume is lower, reducing obstruction to traffic flow. The robot's autonomous operation capability allows it to work during these periods when traditional manual operations would not be feasible due to lighting conditions, thereby optimizing traffic efficiency while maintaining productivity.
Solution Approach 2:
The replacement of manual detection with automated imaging sensors and artificial lighting enables operations during nighttime conditions. The robotic system uses its own illumination sources and electronic sensors to detect cracks independently of ambient daylight, allowing traffic to flow freely during daytime while the robot operates during off-peak hours to minimize disruption.
Data Source
AI summary
A method for programming a robot to autonomously fill cracks in a pavement. The robot fills the cracks using a nozzle having a puck width. The method comprises the steps of obtaining an image of the pavement, identifying one or more crack regions in the pavement from the image, generating a path to fill the one or more crack regions, determining a volume of sealant to fill the one or more crack regions along the path, generating instructions to fill the one or more crack regions using the path and the volume of the sealant, and sending the instructions to the robot.