Over the last years, there is a growing need for climbing robots performing autonomous inspection tasks of large-scale infrastructure, to reduce inspection time and the overall operation costs. Thickness measurement, visual inspection, fault detection, etc. are a few examples of inspection and maintenance applications that could be performed autonomously by robotic platforms like climbing robots. One of the main challenges of inspecting large infrastructures, is the problem of path planning, as the path should be optimal to reduce the inspection time, incorporate sensor properties, and account for important robot requirements such as power supply cabling. This article proposes a novel path planner targeting inspection tasks, where the restrictions posed by the cabling on a Vortex Robot (VR), the attached sensor, and the properties of the scanned surfaces are taken into consideration. The presented framework is successfully evaluated in multiple closed-loop experiments, under different surface inclinations and VR orientations to demonstrate the efficacy of the path planning and control scheme.