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The problem of navigating a formation of interconnected tethered drones, named STEM (System of TEthered Multicopters), in an unknown environment is considered. The tethers feed electrical power from a ground station to the drones and also serve as communication links. The presence of more than one interconnected drone provides enough degrees of freedom to navigate in a cluttered area. The leader drone in the formation must reach a given point of interest, while the followers must move accordingly, avoiding interference with the obstacles. The challenges are the uncertainty in the environment, with obstacles of unknown shape and position, the use of LiDAR (Light Detection And Ranging) sensors, providing only partial information of the surroundings of each drone, and the presence of the tethers, which must not impact with the obstacles and pose additional constraints to how the drones can move. To cope with these problems, a novel real-time planning algorithm based on numerical optimization is proposed: the reference position of each drone is chosen in a centralized way via a convex program, where the LiDAR scans are used to approximate the free space and the drones are moved towards suitably defined intermediate goals in order to eventually reach the point of interest. The approach is successfully tested in numerical simulations with a realistic model of the system.

Multicopter drones equipped with cameras can perform rapid inspections of large buildings, including those with hard to reach features, like bridge pylons. Drones can be made autonomous by providing them with a method to choose a path that maximizes the collected information during the limited flight time allowed by the battery. It is therefore crucial to optimize the trajectories to minimize inspection time. The problem of finding an approximately optimal path passing through a series of desired inspection points in a three-dimensional environment with obstacles is considered. A hierarchical approach is proposed, where the space containing the inspection points is partitioned into different regions and multiple instances of the TSP (Travelling Salesman Problem) are solved, decreasing the overall complexity. An extended graph is used in the TSP, in order to tackle the problem of collision avoidance while planning the trajectory between point pairs. This approach leads to an efficient and scalable method capable of avoiding obstacles, and significantly reduces the time needed to find an optimal path with respect to non-hierarchical methods. Simulation results highlight these features.

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