Energy Efficient Path Planning for 3D Aerial Inspections

Abstract

The use of Unmanned Aerial Vehicles (UAVs) in different inspection tasks is increasing. This technology reduces inspection costs and collects high quality data of distinct structures, including areas that are not easily accessible by human operators. However, the reduced energy available on the UAVs limits their flight endurance. To increase the autonomy of a single flight, it is important to optimize the path to be performed by the UAV, in terms of energy loss. Therefore, this work presents a novel formulation of the Travelling Salesman Problem (TSP) and a path planning algorithm that uses a UAV energy model to solve this optimization problem. The novel TSP formulation is defined as Asymmetric Travelling Salesman Problem with Precedence Loss (ATSP-PL), where the cost of moving the UAV depends on the previous position. The energy model relates each UAV movement with its energy consumption, while the path planning algorithm is focused on minimizing the energy loss of the UAV, ensuring that the structure is fully covered. The developed algorithm was tested in both simulated and real scenarios. The simulated experiments were performed with realistic models of wind turbines and a UAV, whereas the real experiments were performed with a real UAV and an illumination tower. The inspection paths generated presented improvements over 24% and 8%, when compared with other methods, for the simulated and real experiments, respectively, optimizing the energy consumption of the UAV.

Strategies for Optimized UAV Surveillance in Various Tasks and Scenarios: A Review

“…The focus of the above research is on improving the surveillance and detection of objects, while other studies on transmission lines take a more global approach. The upcoming research focuses more on groups of detected objects and developing efficient methods for system-wide detection, primarily through path planning strategies [55,56]. Several studies have proposed sampling-based path planning, such as RRT, RRT*, PRM algorithms, etc., which have the advantage of being able to quickly cover the formulated scope of work and converge to have a solution [57][58][59].…”