![]() The presented trajectory planning method offers flexibility in various setup parameters and is applicable to real-world inspection tasks. It successfully achieves coverage during the visual inspection of complex structures such as a wind turbine and a bridge, outperforming a state-of-the-art method by allowing more surface area to be inspected under the same conditions. The method generates trajectories using a potential field and implements distance fields to prevent collisions and to determine UAVs’ camera orientation. This paper presents trajectory planning for three-dimensional autonomous multi-UAV volume coverage and visual inspection of infrastructure based on the Heat Equation Driven Area Coverage (HEDAC) algorithm. The application of autonomous UAVs to infrastructure inspection tasks provides benefits in terms of operation time reduction, safety, and cost-effectiveness. The path cost is 167.3s\documentclass coordinate system is employed (Color figure online) The inspection path was computed based on a rough CAD model and the polyhedric obstacle was also included. To thoroughly evaluate this new path planning strategy, a set of large-scale simulation scenarios was considered, followed by multiple real-life experimental test-cases using both vehicle configurations.Įxperimental study of the inspection of a subset of the ETH Polyterrasse truncated cones. Both fixed-wing as well as rotorcraft aerial robot configurations are supported and their motion constraints are respected at all optimization steps, while the algorithm operates on both mesh- and occupancy map-based representations of the environment. ![]() ![]() ![]() The algorithm supports the integration of multiple sensors with different fields of view, the limitations of which are respected. The proposed approach is capable of computing short inspection paths via an alternating two-step optimization algorithm according to which at every iteration it attempts to find a new and improved set of viewpoints that together provide full coverage with decreased path cost. This paper presents a new algorithm for three-dimensional coverage path planning for autonomous structural inspection operations using aerial robots. ![]()
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