Abstract:Abstract. UAV technology has become a useful tool for the inspection of infrastructures. Structural Health Monitoring methods are already implementing these vehicles to obtain information about the condition of the structure. Several systems based on close range remote sensing and contact sensors have been developed. In both cases, in order to perform autonomous missions in hard accessible areas or with obstacles, a path planning algorithm that calculates the trajectory to be followed by the UAV to navigate th… Show more
“…If the path planning algorithm presented in this manuscript is compared with the previous A* implementation [ 34 ], an average speed up to 213 times higher is achieved. Table 2 shows the comparison of the execution times of both methods, the traditional A* and the method presented in this manuscript, comparing them.…”
Section: Resultsmentioning
confidence: 99%
“…In this way, when a new path is calculated, just the info from the rooms that the system has to go through is used, making the method more efficient. Preliminary results have been published in a congress [ 34 ]. During the development of this previous path planning algorithm, authors noticed that the algorithm was too slow to be implemented in a real study case, as it does not fulfil the execution time restrictions to be considered as a real-time path planner.…”
Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.
“…If the path planning algorithm presented in this manuscript is compared with the previous A* implementation [ 34 ], an average speed up to 213 times higher is achieved. Table 2 shows the comparison of the execution times of both methods, the traditional A* and the method presented in this manuscript, comparing them.…”
Section: Resultsmentioning
confidence: 99%
“…In this way, when a new path is calculated, just the info from the rooms that the system has to go through is used, making the method more efficient. Preliminary results have been published in a congress [ 34 ]. During the development of this previous path planning algorithm, authors noticed that the algorithm was too slow to be implemented in a real study case, as it does not fulfil the execution time restrictions to be considered as a real-time path planner.…”
Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.
“…In Ref. [25], the authors present a UAV path planning algorithm developed to navigate indoors and outdoors, useful for Structural Health Monitoring (SHM), which calculates waypoints and vehicle orientation for each location based on Voxelization. The paper [13] presents an algorithm that allows a UAS to provide continuous uninterrupted structural inspection service, suitable for use in multi-UAV waypoint missions.…”
Traditional methodologies for precise inspection of bridges (pavement, beams, column cap, column, joints and inside box girder, etc.) with By-bridge equipment, Aerial Work Platform (AWP) or via ropes have several limits that can be overcome by using Unmanned Aerial Vehicles (UAVs). The constant development in this field allows us to go beyond the manual control and the use of a single UAV. In the context of inspection rules, this research provides new inputs to the multilevel approach used today and to the methods of structural inspection with drones. Today, UAV-based inspections are limited by manual and/or semi-automatic control with many restrictions on trajectory settings, especially for areas of difficult access with Global Navigation Satellite Systems (GNSS) denied that still require the intervention of a human operator. This work proposes the use of autonomous navigation with a fleet of UAVs for infrastructural inspections. Starting from a digital twin, a solution is provided to problems such as the definition of a set of reference trajectories and the design of a position controller. A workflow to integrate a generic Bridge Management System (BMS) with this type of approach is provided.
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