Papa, Rafael

The unmanned aerial vehicle (UAV) technology has evolved considerably in recent years and the global demand for package delivery is expected to grow even more during COVID-19 and the social distance era. The low cost of acquisition, payload capacity, maneuverability, and the ability to y at low-altitude with a very low cost of operation, make UAVs a perfect fit to revolutionize the payload transportation of small items. The large-scale adoption of drone package delivery in high-density urban areas can be challenging and the Unmanned Aircraft Systems (UAS) operators must ensure safety, security, efficiency and equity of the airspace system. In order to address some of these challenges, FAA and NASA have developed a new architecture that will support a set of services to enable cooperative management of low-altitude operations between UAS operators. The architecture is still in its conceptual stage and designing a mechanism that ensures the fair distribution of the available airspace to commercial applications has become increasingly important. Considering that, the path planning is one of the most important problems to be explored. The objective is not only to find an optimal and shortest path but also to provide a collision-free environment to the UAVs. Taking into consideration all these important aspects and others such as serving on-demand requests, flight duration limitation due to energy constraints, maintaining the safety distance to avoid collisions, and using warehouses as starting and ending points in parcel delivery, this dissertation proposes: (i) an energy-constrained scheduling mechanism using a multi-source A* algorithm variant, and (ii) a generalized path planning mechanism using a space-time graph with multi-source multi-destination BFS generalization to ensure pre-flight UAV collision-free trajectories. This dissertation also uses the generalized path planning mechanism to solve the energy-constrained drone delivery problem. The experimental results show that the proposed algorithms are computationally efficient and scalable with the number of requests and graph size.

Energy e ciency is a critical constraint in wireless sensor networks. Wireless
sensor networks (WSNs) consist of a large number of battery-powered sensor nodes,
connected to each other and equipped with low-power transmission radios. Usually,
the sensor nodes closer to the sink are more likely to become overloaded and subject
to draining their battery faster than the nodes farther away, creating a funneling
e ect. The use of a mobile device as a sink node to perform data gathering is a
well known solution to balance the energy consumption in the entire network. To
address this problem, in this work we consider the use of an UAV as a mobile sink.
An unmanned aircraft vehicle (UAV) is an aircraft without a human pilot on-board,
popularly known as a Drone.
In this thesis, besides the use of the UAV as a mobile sink node, we propose an
UAV-aided algorithm for data gathering in wireless sensor networks, called Humming-
bird. Our distributed algorithm is energy-e cient. Rather than using an arbitrary
path, the UAV implements an approximation algorithm to solve the well-known NP-
Hard problem, the Traveling Salesman Problem (or TSP), to setup the trajectory of
node points to visit for data gathering. In our approach, both the path planning and the data gathering are performed by the UAV, and this is seamlessly integrated with
sensor data reporting.
The results, using ns-3 network simulator show that our algorithm improves
the network lifetime compared to regular (non-UAV) data gathering, especially for
data intensive applications.