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Projecte llegit

Títol: Three-Dimensional Risk-Aware Path Planning for Unmanned Aerial Vehicles in Urban Environments

Estudiants que han llegit aquest projecte:

Director/a: GIL PONS, PILAR

Departament: FIS

Títol: Three-Dimensional Risk-Aware Path Planning for Unmanned Aerial Vehicles in Urban Environments

Data inici oferta: 27-10-2020     Data finalització oferta: 27-10-2020


Estudis d'assignació del projecte:
    DG ENG AERO/SIS TEL
Tipus: Individual
 
Lloc de realització: ERASMUS
 
        Supervisor/a extern: Giorgio Guglieri
        Institució/Empresa: Politecnico di Torino
        Titulació del Director/a: PhD., Eng.
 
Paraules clau:
unmanned aerial vehicles, drones, risk-based maps, risk, safety, path planning, path planner, algorithm
 
Descripció del contingut i pla d'activitats:
The use of unmanned aerial vehicles (more popularly known as
drones) has experienced a fast growth in the last two decades,
especially over urban areas, but they have been subjected to
strong regulations by the authorities due to safety concerns. For
this reason, the drone industry must develop suitable strategies
to take full advantage of the uncountable drone's benefits.

In line with these initiatives, this thesis focuses on the
development of a risk-aware path planning for Unmanned Aerial
Vehicles (UAS) in urban environments. Given a start and a target
position and a 2.5-dimensional risk-based map, the path planner
is able to find the optimal path minimizing the effective risk
and the path length.

The 2.5-dimensional risk-based map quantifies the risk caused by
the UAS to the population on the ground computed at discrete
flight altitudes, as well as defining no-fly zones and obstacles
at the flight altitude. The path planner is based on the well-
known RRT* (Rapidly-exploring Random Tree) algorithm and it is
implemented using the ROS (Robot Operating System) framework.

Moreover, the proposed approach is extended considering the
Quality of Service of the mobile network used to communicate with
the aircraft. In this scenario, the path planner performs an
optimization to minimize the risk and to guarantee a
communication channel with a minimum Quality of Service.

Finally, an energy-aware optimization is also carried out in
order for the path planner to seek for less consuming routes,
performing an overall tri-objective optimization, seeking for the
most optimal path in terms of risk, coverage and energy.
 
Overview (resum en anglès):
Context: The use of unmanned aerial vehicles (more popularly known as drones) has experienced a fast growth in the last two decades, especially over urban areas. This led to important safety concerns and thus to the development of a body of astringent regulations aimed to minimize eventual hazards. For this reason, the drone industry must develop suitable strategies to take full advantage of the use of drones, while simultaneously meeting safety and legal requirements.

Goals: In line with the former concerns and opportunities, this work focuses on the development of an efficient risk-aware path planning for Unmanned Aerial Systems (UAS) in urban environments.

Methods: We use a well-known open-source robotics middleware Robot Operating System (ROS), together with The Open Motion Planning Library, which allow to solve a wide range of complex motion planning problems. Specifically, our proposed strategy is based on the Rapidly-Exploring Random Tree ¿Star¿ algorithm, which allows rapid and efficient exploration of the different paths and, ultimately, convergence to an optimal solution. Our work is actually a 3-dimensional extension of 2-dimension risk-based path planner developed at the Politecnico di Torino. This extension, and some additional optimization goals implied structural changes in the original code which had to be carefully implemented and tested.

Results: Given a start and a target position, as well as a 2.5-dimensional risk-based map, the path planner is able to find the optimal path while minimizing the effective risk. The 2.5-dimensional risk-based map quantifies the risks of operating UAS eventually affecting the population on the ground (computed at discrete flight altitudes). In addition, it defines no-flight zones and obstacles at the considered flight altitude. Moreover, the originally proposed approach was extended to consider the Quality of Service of the mobile network used to communicate with the aircraft. In this scenario, the path planner performs an optimization to minimize the risk and to guarantee a communication channel with a minimum Quality of Service. Finally, an energy-aware optimization was also carried out for the path planner to be able to seek for less consuming routes.

Conclusions: We met our main goal of devising a method for planning simultaneously efficient and safe UAS paths, and extended it by performing an overall tri-objective optimization, seeking for the optimal path in terms of risk, coverage and energy.

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