3 research outputs found
A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs
This paper describes a bioinspired neural-network-based approach to solve a coverage
planning problem for a fleet of Unmanned Aerial Vehicles exploring critical areas. The main goal is
to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions
between vehicles and other obstacles. This specific task is suitable for surveillance applications,
where the uniform distribution of the fleet in the map permits them to reach any position on the
map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network
structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron
has a local cost and has a local connection only with neighbor neurons. The cost of each neuron
influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We
introduce several controls and precautions to minimize the risk of collisions and optimize coverage
planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm
in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed
approach to manage and coordinate the fleet providing the full coverage of the map in every tested
scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map
SORA Methodology for Multi-UAS Airframe Inspections in an Airport
Deploying Unmanned Aircraft Systems (UAS) in safety- and business-critical operations
requires demonstrating compliance with applicable regulations and a comprehensive understanding
of the residual risk associated with the UAS operation. To support these activities and enable the
safe deployment of UAS into civil airspace, the European Union Aviation Safety Agency (EASA) has
established a UAS regulatory framework that mandates the execution of safety risk assessment for
UAS operations in order to gain authorization to carry out certain types of operations. Driven by
this framework, the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) released the
Specific Operation Risk Assessment (SORA) methodology that guides the systematic risk assessment
for UAS operations. However, existing work on SORA and its applications focuses mainly on single
UAS operations, offering limited support for assuring operations conducted with multiple UAS and
with autonomous features. Therefore, the work presented in this paper analyzes the application of
SORA for a Multi-UAS airframe inspection (AFI) operation, that involves deploying multiple UAS
with autonomous features inside an airport. We present the decision-making process of each SORA
step and its application to a multiple UAS scenario. The results shows that the procedures and safety
features included in the Multi-AFI operation such as workspace segmentation, the independent
multi-UAS AFI crew proposed, and the mitigation actions provide confidence that the operation can
be conducted safely and can receive a positive evaluation from the competent authorities. We also
present our key findings from the application of SORA and discuss how it can be extended to better
support multi-UAS operations.UniĂłn Europea 10101725
Planning to Monitor Wildfires with a Fleet of UAVs
International audienceWe present an approach to plan trajectories for a fleet of fixed-wing UAVs to observe a wildfire evolving over time. Realistic models of the terrain, of the fire propagation process, and of the UAVs are exploited, together with a model of the wind. The approach tailors a generic Variable Neighborhood Search method to these models and associated constraints. Simulation results show ability to plan observation trajectories for a small fleet of UAVs, and to update the plans when new information on the fire are incorporated in the fire model