Contour-guided zigzag path planning for the automation of pothole spray repair based on the depth image

ABSTRACT

Current pavement pothole repair technology relies heavily on human experience and manual operation. To automate the repair process of pavement pothole spraying repair, a contour-guided zigzag path planning method based on depth image was proposed in this paper, including pothole slicing and slice repair path planning. In the aspect of pothole slicing, a double flood filling method was proposed to ensure the repair strategy from bottom to up under the priority of region by region, while in the aspect of slice path planning, a contour-guided round-trip repair path planning method was proposed to arrange the repair trajectory homogeneously in the pothole without exceeding its irregular boundary. The path planning results showed that the average spacing error of four kinds of common potholes with different shapes was 7.31%, the average time of the whole path planning process was 1.571 s, the average relative difference of the mean square error was only 1.83 mm, which indicated that the proposed method could obtain high path planning accuracy and fast path planning speed. The results demonstrated that the proposed contour-guided zigzag path planning method based on depth image could be applied to the automatic spraying repair of different potholes with convex or concave contours and achieve good pavement repair flatness.

KEYWORDS:

• Pavement pothole
• automated spraying repair
• path planning
• depth image

Acknowledgments

The authors would like to thank the support provided by the National Engineering Laboratory for Highway Maintenance Equipment and the Key Laboratory of Road Construction Technology and Equipment, Ministry of Education.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 61901056]; Natural Science Basic Research Plan in Shaanxi Province of China [grant number 2021JQ264,2021JQ282]; Fundamental Research Funds for the Central Universities, CHD [grant number 300102251104].