Unmanned aerial vehicle–human collaboration route planning for intelligent infrastructure inspection

Motivated by the strengths of unmanned aerial vehicle (UAV), the UAV–human collaboration route planning (UHCRP) for intelligent infrastructure inspection is a problem worthy of discussion to help reduce human costs and minimize the risk of noninspected infrastructures under limited resources. To facilitate UHCRP, this paper proposes a novel deep reinforcement learning (DRL)-based approach to well handle multi-source uncertain features and constraints at a fast speed. To begin with, UHCRP is mathematically described and reformulated as a dual interdependent deep reinforcement learning (diDRL) framework to reflect real-world scenarios. Afterward, a novel policy network named the attention-based deep neural network (A-DNN) is introduced to learn the route planning decisions for the combinatorial optimization problem. In particular, A-DNN is made up of an encoder and a dual decoder for UAV and human inspection, where the multi-head attention mechanism is incorporated to generate richer representations for model performance improvement. Performance of the proposed dual multi-head attention model (DAM) has been tested in simulations and a real-world case study regarding wind farm inspection. Results indicate that DAM under the sampling decoding strategy can deliver a high-quality path plan and show better generalizability for larger scale problem sizes compared to single-head attention model (SAM), multi-head attention model (AM), and two baseline models, namely OR-Tools and genetic algorithm. Moreover, DAM trained by randomly generated data can be directly employed to solve the practical problem with standardization of inputs. Overall, DRL integrates decision-making for inspection method selection and inspected infrastructure selection, providing adaptive and intelligent inspection path planning for UAV and human in complex and dynamic engineering environments.