Coverage path planning for a flock of aerial vehicles to support autonomous rovers through traversability analysis

Authors

DOI:

https://doi.org/10.21014/acta_imeko.v8i4.680

Abstract

In rough terrains, such as landslides or volcanic eruptions, it is extremely complex to plan safe trajectories for an Unmanned Ground Vehicle (UGV), since both robot stability and path execution feasibility must be guaranteed. In this paper, we present a complete solution for the autonomous navigation of ground vehicles in the mentioned scenarios. The proposed solution integrates three different aspects. The first is the coverage path planning for the definition of UAV trajectories for aerial imagery acquisition. The collected images are used for the photogrammetric reconstruction of the considered area. The second aspect is the adoption of a flock of UAVs to implement the coverage in a parallel way. In fact, when non-coverable zones are present, decomposition of the whole area to survey is performed. A solution to assign the different regions among the flying vehicles composing the team is presented. The last aspect is the path planning of the ground vehicle by means of a traversability analysis performed on the terrain 3D model. The computed paths are optimal in terms of the difficulty of moving across the rough terrain. The results of each step within the overall approach are shown.

Author Biographies

Dario Calogero Guastella, University of Catania

Department of Electrical, Electronics and Computer Engineering

Post Doctoral Research Fellow

Luciano Cantelli, University of Catania

Department of Electrical, Electronics and Computer Engineering

Post Ph.D. Researcher

Domenico Longo, University of Catania

Department of Electrical, Electronics and Computer Engineering

Researcher

Carmelo Donato Melita, University of Catania

Department of Electrical, Electronics and Computer Engineering

Post Ph.D. Researcher

Giovanni Muscato, University of Catania

Department of Electrical, Electronics and Computer Engineering

Full Professor

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Published

2019-12-16

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Section

Research Papers