Task Coordination and Trajectory Optimization for Multi-Aerial Systems via Signal Temporal Logic: A Wind Turbine Inspection Study

TL;DR

The paper tackles multi-UAV wind turbine inspection under safety and time-window constraints by formulating task allocation and trajectory generation within a Signal Temporal Logic (STL) framework. It introduces a two-stage hierarchy where a MILP solution seeds a global STL optimizer, and robustness of the STL specifications is optimized to reflect user preferences. An event-triggered replanning mechanism and a generalized robustness scoring method are integrated to handle dynamic disturbances and objective conflicts. Validation via MATLAB, Gazebo, and mock-up field experiments demonstrates safe, feasible, and scalable coordination for comprehensive turbine inspection with full area coverage and collision avoidance. The work advances practical, robust planning for cooperative aerial inspection in complex industrial environments.

Abstract

This paper presents a method for task allocation and trajectory generation in cooperative inspection missions using a fleet of multirotor drones, with a focus on wind turbine inspection. The approach generates safe, feasible flight paths that adhere to time-sensitive constraints and vehicle limitations by formulating an optimization problem based on Signal Temporal Logic (STL) specifications. An event-triggered replanning mechanism addresses unexpected events and delays, while a generalized robustness scoring method incorporates user preferences and minimizes task conflicts. The approach is validated through simulations in MATLAB and Gazebo, as well as field experiments in a mock-up scenario.