Unmanned Aerial Vehicles (UAVs) are increasingly permitted to operate within Visual Line of Sight (VLoS) under EU and US regulations. However, Beyond Visual Line of Sight (BVLoS) operations remain restricted, with waivers or certifications required. Extended Visual Line of Sight (EVLoS) offers a transitional solution, involving trained observers to assist pilots when visibility is obstructed. We propose enhancing EVLoS by integrating ground infrastructure, specifically city cameras and wireless communication networks already available on the ground, to replace human observers and enable BVLoS capabilities. Fixed and mobile cameras track drones to ensure regulatory compliance, while real-time data transmission via communication networks provides indirect oversight. The approach increases operational range, reliability, and redundancy through multi-hop connectivity. We introduce the Minimum Latency Problem (MLP), a UAV multi-trajectory optimization problem where UAVs are constantly tracked and monitored through ground antennas and city cameras, mimicking the human observers in EVLoS. Our goal is to minimize communication latency while ensuring that the number of antennas used for coverage is minimum. We prove MLP is$N P$-hard and propose an algorithm to solve it. Experiments on synthetic data demonstrate the effectiveness of our approach in matching coverage and latency requirements.

We consider Internet of Things (IoT) sensors deployed inside an area to be monitored. Drones can be used to collect the data from the sensors, but they are constrained in energy and storage. Therefore, all drones need to select a subset of sensors whose data are the most relevant to be acquired, modeled by assigning a reward. We present an optimization problem called Multiple-drone Data-collection Maximization Problem (MDMP) whose objective is to plan a set of drones' missions aimed at maximizing the overall reward from the collected data, and such that each individual drone's mission energy cost and total collected data are within the energy and storage limits, respectively. We optimally solve MDMP by proposing an Integer Linear Programming based algorithm. Since MDMP is NP-hard, we devise suboptimal algorithms for single- and multiple-drone scenarios. Finally, we thoroughly evaluate our algorithms on the basis of random generated synthetic data.

Drones have become increasingly popular in a variety of fields, including agriculture, emergency response, and package delivery. However, most drone operations are currently limited to within Visual Line of Sight () due to safety concerns. Flying drones Beyond Visual Line of Sight () broadens to new challenges and opportunities, but also requires new technologies and regulatory frameworks to ensure that the drone is constantly under the control of a remote operator. In this work, we propose a novel graph-based multi-layer framework that closely resembles real-world scenarios and challenges in order to plan drone operations. Our framework includes layers of constraints such as ground risk, cellular network infrastructure, and obstacles, at different heights. From the multi-layer structure, a graph is constructed whose edges are weighted with a dependability score that takes into account the information of the layers, allowing efficient path planning of missions, using algorithms such as Dijkstra’s. Since the built graph can be really large, we also propose lighter graph-based corridors by considering only a limited portion of the original graph. Through extensive experimental evaluation on a real dataset, we demonstrate the effectiveness of our framework in solving the (), which can be efficiently solved by applying the Dijkstra’s algorithm.

The use of drones for collecting information and detecting bugs in orchards covered by nets is a challenging problem. The nets help in reducing pest damage, but they also constrain the drone’s flight path, making it longer and more complex. To address this issue, we model the orchard as an aisle-graph, a regular data structure that represents consecutive aisles where trees are arranged in straight lines. The drone flies close to the trees and takes pictures at specific positions for monitoring the presence of bugs, but its energy is limited, so it can only visit a subset of positions. To tackle this challenge, we introduce the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the orienteering problem, where likely infested locations are prioritized by assigning them a larger profit. Additionally, the drone’s movements have a cost in terms of energy, and the objective is to plan a drone’s route in the most profitable locations under a given drone’s battery. We show that SOAP can be optimally solved in polynomial time, but for larger orchards/instances, we propose faster approximation and heuristic algorithms. Finally, we evaluate the algorithms on synthetic and real datasets to demonstrate their effectiveness and efficiency.

In this work, we investigate the impact of the wind in a drone-based delivery system. For the first time, to the best of our knowledge, we adapt the trajectory of the drone to the wind. We consider a truck-drone tandem delivery system. The drone actively reacts to the wind adopting the “most tailwind” trajectory available between the truck’s path and the delivery. The truck moves on a predefined route and carries the drone close to the delivery point. We propose the Minimum-energy Drone-trajectory Problem (MDP) which aims, when the wind affects the delivery area, at planning minimum-energy trajectories for the drone to serve the customers starting from and returning to the truck. We then propose two algorithms that optimally solve MDP under two different routes of the truck. We also analytically study the feasibility of sending drones with limited battery to deliver packages. Finally, we first numerically compare our algorithms on randomly generated synthetic and real data, and then we evaluate our model simulating the drone’s flight in the BlueSky simulator.

In this paper, we consider the problem of using a drone to collect information within orchards in order to detect bugs. An orchard can be modeled as an aisle-graph, which is a regular data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone’s energy is limited, only a subset of locations in the orchard can be visited with a fully charged battery. Those places that are most likely to be infested should be selected to promptly detect the parasite. We study the budgeted constrained position selection problem in the orchard from an algorithmic point of view. We present the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the well-known orienteering problem where the finite resource is the drone’s battery. We first show that SOAP can be optimally solved for aisle-graphs in polynomial time. However, the optimal solution is not efficient for large orchards. Then, we propose two efficient heuristics that work even for large (orchard) instances. After a thorough analysis of the proposed solutions, we evaluate their performance by simulation experiments on both synthetic and real data sets.

In this paper, we investigate the manner in which energy consumption in drone deliveries is affected by windy environmental conditions. We know in fact that the energy consumption of the drone will depend on the static and dynamic parameters of the scenario where it moves and according to these it decides, during the mission, to detour from the originally planned path to take advantage of the wind changes. In order to validate this, we simulate possible deliveries among fixed destinations relying on a real data-set of recorded winds obtained from different weather stations in Corsica, France. For our analysis, we will mainly concentrate on the evaluation of the delivery scheme proposed in the literature, where completing a delivery means finding a cycle for the drone that is feasible, i.e., that can be completed with the available energy autonomy of the drone.

The impact on the energy consumption of flying drones in favorable winds is investigated in this paper. A tandem system is considered, with only one drone and one truck. The truck moves on a predefined route and brings the drone close to the delivery point. Then, the drone plans its service route by choosing the take-off and landing points from which the delivery will be performed. We propose a constant time algorithm OSR to plan the drone route with minimum-energy service when the truck moves on a line in front of the deliveries (i.e., highway). Then, we devise the algorithm MS-OSR to plan a drone minimum-energy service route when the truck moves on a multiline that bounds a convex area where the deliveries take place. We found that OSR and MS-OSR plan drone service routes that save at least 30% and 60%, respectively, of the energy consumed by connecting the delivery and the truck following the shortest route, that is, following the perpendicular segment between the delivery point and the truck’s route.