AERIAL-CORE: AI-Powered Aerial Robots for Inspection and Maintenance of Electrical Power Infrastructures

Large-scale infrastructures are prone to deterioration due to age, environmental influences, and heavy usage. Ensuring their safety through regular inspections and maintenance is crucial to prevent incidents that can significantly affect public safety and the environment. This is especially pertinent in the context of electrical power networks, which, while essential for energy provision, can also be sources of forest fires. Intelligent drones have the potential to revolutionize inspection and maintenance, eliminating the risks for human operators, increasing productivity, reducing inspection time, and improving data collection quality. However, most of the current methods and technologies in aerial robotics have been trialed primarily in indoor testbeds or outdoor settings under strictly controlled conditions, always within the line of sight of human operators. Additionally, these methods and technologies have typically been evaluated in isolation, lacking comprehensive integration. This paper introduces the first autonomous system that combines various innovative aerial robots. This system is designed for extended-range inspections beyond the visual line of sight, features aerial manipulators for maintenance tasks, and includes support mechanisms for human operators working at elevated heights. The paper further discusses the successful validation of this system on numerous electrical power lines, with aerial robots executing flights over 10 kilometers away from their ground control stations.

Automating Bird Diverter Installation through Multi-Aerial Robots and Signal Temporal Logic Specifications

This paper tackles the task assignment and trajectory generation problem for bird diverter installation using a fleet of multi-rotors. The proposed motion planner considers payload capacity, recharging constraints, and utilizes Signal Temporal Logic (STL) specifications for encoding mission objectives and temporal requirements. An event-based replanning strategy is introduced to handle unexpected failures and ensure operational continuity. An energy minimization term is also employed to implicitly save multi-rotor flight time during installation. Simulations in MATLAB and Gazebo, as well as field experiments, demonstrate the effectiveness and validity of the approach in a mock-up scenario.

A Signal Temporal Logic Motion Planner for Bird Diverter Installation Tasks with Multi-Robot Aerial Systems

This paper investigates the problem of task assignment and trajectory generation for the installation of bird diverters with a fleet of multirotors leveraging on Signal Temporal Logic (STL) specifications. We extend our previous motion planner to compute feasible and constrained trajectories, taking into account payload capacity limitations and recharging constraints. The proposed planner ensures the continuity of the operation, while guaranteeing compliance with safety requirements and mission fulfillment. Additionally, an event-based replanning strategy is proposed to react to unforeseen failures. An energy minimization term is also considered to implicitly save multirotor flight time during installation operations. Numerical simulations in MATLAB, Gazebo, and field experiments demonstrate the performance of the approach and its validity in mock-up scenarios.