Strategies for autonomous robot to inspect pavement distresses

Distress inspection is an important task in pavement maintenance. Pavement inspection requires tremendous human resources, so many investigators start developing automatic and robotic inspection methods to increase the efficiency and accuracy. However, the systems they developed are applicable for network-level inspection (large areas, long-distance) but too expensive or too big for project-level inspection (small areas, short-distance). Dealing with this problem, some researchers have developed an autonomous vehicle for inspection. In this research, we specific focus on developing strategies for executing the inspection tasks using robots. We developed three strategies. The first strategy is random-walk. The second strategy is random-walk with map recording. The third one adds the vision capacity to the robot. To validate the three strategies, we developed a test field in a virtual environment. This test field includes 5 types of common distress, including an alligator crack, a patching, a breaking hole, a rectangular manhole and a circular manhole. We also developed a virtual robot which can autonomously navigate in the test field. We then implemented the three survey strategies in the robot and compare their performances with traditional longitudinal survey method. The results show that using the first strategy, we can increase frequency for passing the distresses; it means that robot can detect and collect data more times than traditional longitudinal survey. The results of the second strategy show that we can increase the repeatability by using map recording to guide random-walk. The results of third strategy show that the robot can find more distresses in a certain amount of time; it means that we can improve survey efficiency by adding vision capacity to adjust motion path when distresses detected. Comparing these strategies with conventional Longitudinal survey strategy(L strategy), the three proposed strategies have a higher possibility of revisiting distre- - sses, and it means making the results more reliable.