# Path planning of unmanned aerial vehicle flying at low height for Internet of underground things -Application to data collection of buried communicating sensor nodes in agriculture

Christophe Cariou, Laure Moiroux-Arvis, Fatiha Bendali, Yuankang Hu, Jean Mailfert

<figure><img src="image_1.png" /></figure>

## To cite this version:

Christophe Cariou, Laure Moiroux-Arvis, Fatiha Bendali, Yuankang Hu, Jean Mailfert. Path planning of unmanned aerial vehicle flying at low height for Internet of underground things -Application to data collection of buried communicating sensor nodes in agriculture. 10th International conference on smart and sustainable technologies, University of Split, Jun 2025, Split - Bol (Island of Brac), Croatia. 10.23919/SpliTech65624.2025.11091712. hal-05157527

HAL Id: hal-05157527

https://hal.inrae.fr/hal-05157527v1

Submitted on 11 Jul 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Path planning of Unmanned Aerial Vehicle flying at low height for Internet of Underground Things - Application to data collection of buried communicating sensor nodes in agriculture

Christophe Cariou

Laure Moiroux-Arvis

Fatiha Bendali

Université Clermont Auvergne

Université Clermont Auvergne

Université Clermont Auvergne

INRAE, UR TSCF

INRAE, UR TSCF

LIMOS, UMR CNRS 6158

Aubière, France

Aubière, France

Aubière, France

christophe.cariou@inrae.fr

laure.moiroux-arvis@inrae.fr

fatiha.bendali-mailfert@limos.fr

Yuankang Hu

Université Clermont Auvergne

LIMOS, UMR CNRS 6158

Aubière, France

Yuankang.HU@doctorant.uca.fr

Jean Mailfert

Université Clermont Auvergne

LIMOS, UMR CNRS 6158

Aubière, France

jean.mailfert@uca.fr

**Abstract**—Buried communicating sensor nodes are increasingly used in Internet of Underground Things to measure and transmit soil moisture and temperature information. This is particularly the case in agriculture to remotely monitor soil conditions and optimize cultivation interventions. However, as the radio waves are significantly more attenuated in the soil in comparison with in the air, the data collection of these nodes is problematic due to their very short-range communications. This paper focuses on the use of Unmanned Aerial Vehicles (UAVs) to collect the data. By flying at low height, UAVs can in fact come close to the nodes to collect the stored measurements. That requires however to consider the obstacles in the path planning algorithm and solve the Close Enough Traveling Salesman Problem (CE-TSP) in case many nodes need to be visited. To answer these problems, this paper first establishes the normalized Digital Surface Model (nDSM) based on an airborne High Density LiDAR sensor. This model is used to study the optimal path for the UAV having to visit a large number of nodes. To that end, the cost matrix defining the Euclidean distances of the trajectories connecting each pair of nodes is established. If one or more obstacles are detected between two nodes, two strategies are evaluated: the first one studies the possibility to fly above obstacles, the second one bypasses obstacles on the left or right sides. From the knowledge of this cost matrix, a Genetic Algorithm (GA) completed with heuristic rules is finally applied to solve the CE-TSP. The results are presented on a real use case, considering 100 buried sensor nodes distributed in our experimental farm and considering the real maximum time flight of our UAV. These results clearly highlight the relevance of the methods proposed and open the way for increased surveillance of underground spaces in agriculture.

**Index Terms**—UAV, IoUT, CE-TSP, digital surface model, genetic algorithm, agriculture

This work was supported by the International Research Center "Innovation Transportation and Production Systems" of the I-SITE CAP 20-25, and by the French National Research Agency, OCOD project n°ANR-24-CE25-7644.

## I. INTRODUCTION

In recent years, the development of buried communicating sensor nodes in the natural environment has been increasingly put forward, particularly for measuring soil moisture and temperature, but also salinity and vibrations, see Fig. 1a and [1]–[4]. This approach led to the concept of Internet of Underground Things (IoUT) [5]. By completely burying the nodes at several tens of centimeters deep, they do not interfere with human activities, and are protected from animals, malicious acts and collisions with vehicles. The applications are numerous: they range from smart irrigation, precision farming, landslide monitoring, leak detection of storage tanks, to the monitoring of ground conditions on sports fields [6]–[8].

However, the communication ranges of underground nodes are often limited to a few meters as the radio electromagnetic waves are significantly more attenuated in the soil in comparison with in the air (20–300 times worse), see [9]. In addition,

<figure><img src="image_3.png" /><figcaption>Fig. 1: a) Buried communicating sensor node before being covered with soil, LoRa communication at 868 MHz [1], b) Modeling of the communication range of the node above the ground, and data collection by means of a UAV</figcaption></figure>
to minimize the energy consumption of the nodes and thus extend their lifespan, the transmit power of the radio module needs to be tuned to the minimum. The transmit time must also be reduced, e.g. by limiting the Spreading Factor in LoRa communications [10] and activating the radio transmission module for the shortest possible time.

The data collection task of buried communicating sensor nodes is therefore problematic. A solution is to install a gateway or a data collector close to each node. However, the collection distance can be very short, e.g. only a few meters if a signal of wake-up needs to be sent to the nodes [11]. This paper therefore proposes an original approach based on a data collector embedded on a Unmanned Aerial Vehicle (UAV), see our previous work [12]. In fact, UAVs are more and more used for various applications to enhance coverage, reliability, and energy efficiency of wireless networks [13]. With their increasing performances in autonomy and payload, they are today particularly relevant to perform the data collection task of a large number of buried sensor nodes.

However, at low flying height, the risk of collision of the UAV with an obstacle in the environment is high. In addition, in case of a large number of nodes to be visited, the Close Enough Traveling Salesman Problem (CE-TSP), which is a well-known theoretical problem of combinatorial optimization [14]–[16], has to be addressed to search the optimal flight trajectory. In this case, the UAV has to successively enter into the communication ranges of each buried sensor node to collect the data, see Fig. 1b. The problem is that it is necessary to have a previous and accurate knowledge of the main obstacles present in the workspace of the UAV to, on the one hand, define efficient strategies to safely collect the data of the buried sensor nodes and, on the other hand, optimize the flight trajectory of the UAV to visit all the nodes.

To address these issues, the contribution of this paper is to extend our previous work [12] and [17] by establishing the normalized Digital Surface Model (nDSM) of the UAV workspace and using it with a Genetic Algorithm (GA) to solve the CE-TSP with obstacles. The nDSM is built from real data obtained with an airbone High Density LiDAR sensor. It is converted into an image representing the obstacles for the UAV: morphological operations (dilation) are then applied to the image to add a safe lateral distance to the boundaries of obstacles (i.e the obstacles are considered slightly larger than they really are). This image and the nDSM are next used to study the optimal path for the UAV having to visit a large number of nodes. To that end, the cost matrix defining the Euclidean distances of the trajectories connecting each pair of nodes is established. If one or more obstacles are detected between two nodes, two strategies are evaluated: the first one calculates the required distance to fly above obstacle area, the second one bypasses the obstacle area from one side.

This paper is organized as follows. Section II presents the construction of the nDSM from airbone High Density LiDAR data. Section III presents the exploitation of this information to determine the strategies to bypass obstacle areas. Section IV presents the Genetic Algorithm (GA) and the heuristic

rules developed to solve the CE-TSP. Section V presents the results of the approach proposed, applied on a real-world scenario involving 100 buried sensor nodes distributed in our experimental farm. Finally, conclusion and future research directions are presented in Section VI.

## II. AIRBONE HIGH DENSITY LIDAR

An airborne LiDAR sensor (Light Detection and Ranging) is a remote sensing technology that uses laser light to measure distances from an aircraft to the Earth's surface. The sensor emits rapid pulses of laser light and measures the time it takes for each pulse to bounce back after hitting the ground or other objects. By calculating these distances, accurate three-dimensional points of the ground, vegetation, and buildings can be obtained, see Fig. 2a.

The French National Geographic Institute (IGN) is today scanning the entire French territory with an airborne High Definition LiDAR (this represents about seven thousand hours of flight). The result is trillions of georeferenced 3D points with a high resolution (10 to 25 points per square meter). The points are expressed in Lambert 93 coordinates, and each point is classified as belonging to the ground, vegetation, buildings or water surface with respect to the intensity of the received signal. Our approach is to exploit this point cloud to accurately determine the position and height of the obstacles in the workspace of the UAV. To that end, the points corresponding to our experimental farm are first downloaded, see Figs. 2b and 2c. By loading these points into the QGIS software, see Fig. 3a (our workspace is an area of size 1400 m × 1500 m), the objective is then to create the nDSM with a resolution of 1 m, by following the principle presented in Fig. 3b.

<figure><img src="image_4.png" /><figcaption>Fig. 2: a) Cloud of 3D points (10 points per square meter) of our experimental farm (INRAE) from the airbone HD LiDAR sensor of the French National Geographic Institute (IGN). The raw data is freely accessible on the internet (https://geoservices.ign.fr/lidarhd), b) Our experimental farm, INRAE, Google Earth © Digital Globe, 46°20′22.35″N, 3°25′44.28″E, c) LiDAR data distributed in sets of size 1000 m × 1000 m (10 millions of points per set): our experimental farm required to download four data sets.</figcaption></figure>
### III. USE CASE AND STRATEGIES

#### A. Use case

A use case composed of 100 buried communicating sensor nodes distributed in the nDSM is considered, see the location of the nodes in Fig. 6. The communication range of each node is limited to 10 m, that corresponds to a suitable distance to wake-up the nodes from a signal sent by the UAV. The objective is then to study the optimal path for the UAV having to visit all the nodes. The UAV used to perform this mission is our rotary-wing UAV, see Figs. 7 and 8.

<figure><img src="image_5.png" /><figcaption>Fig. 3: a) Point cloud: the red points belong to the buildings, the green points belong to the vegetation (low, medium and high), the white points belong to the water surface and brown points belong to the ground, b) Principal to determine the nDSM</figcaption></figure>

The Digital Elevation Model (DEM) which contains only the points belonging to the ground, is subtracted to the Digital Surface Model (DSM), see Figs. 4a and 4b. The result is the nDSM, see Fig. 4c: this model contains the height of the obstacles above the ground (and not the altitude as in the DSM). The nDSM is next used to create an image representing the obstacles at low height for the UAV, see Fig. 5a. By applying mathematical morphology tools on the image (dilation with a structuring element 3×3 square), a safe lateral distance for the UAV can thus be easily added to the boundaries of obstacles (i.e. the obstacles are considered larger than they really are), see Fig. 5b.

<figure><img src="image_6.png" /><figcaption>Fig. 4: a) Digital Surface Model (DSM), b) Digital Elevation Model (DEM), c) normalized Digital Surface Model (nDSM)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5: a) Image of the obstacles for the UAV at low height: the black pixels correspond to obstacles (height greater than 1 meter), the white pixels correspond to free space, b) Image after dilation (2 times with a structuring element 3×3 square)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 6: Use case: the red points represent the positions of 100 buried communicating sensor nodes. The size of the points corresponds to the communication range of the nodes (10 m).</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7: Experimental UAV: Quadrirotor Tundra (Hexadrone), wingspan 1.83 m, payload 4 kg, battery Lithium-polymer 22.2 V - 30 A/h, maximum speed 18 m/s.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 8: Energy consumption of the UAV during flight.</figcaption></figure>
An essential parameter for the path planning algorithm is the maximal flying time of the UAV. To estimate this time, its real energy consumption was measured by embedding a current clamp directly connected to the battery (the clamp is visible on Fig. 7). The results are shown in Fig. 8. The energy consumption is about 40 A during the geostationary flight and when the UAV is slowly moving. With its battery of 30 A/h, the theoretical maximal flying time is therefore 50 minutes in this flying condition (the current clamp weights 640 g). However, it should be noticed that, at a percentage of battery equals to 20 %, the UAV is configured to stop its mission and perform a Return To Launch (RTL) procedure. That means that the mission of our UAV must not exceed 40 minutes for the data collection task (in the best case as the energy consumption of UAV is impacted by numerous factors as the wind force, the air temperature, and the flying dynamics).

## B. Strategies to bypass the obstacles

To determine the optimal path in terms of Euclidean distance for the UAV having to visit the $n = 100$ nodes, the first objective is to calculate the cost matrix (matrix $n \times n$) for all the line segments relying two nodes $p_i$ and $p_j$, and this at the minimal flying height allowed for the UAV ($h_{uav} = 5$ m), see Fig. 9a. At this height, the knowledge of the nDSM and the image of obstacles presented in the section II is used to determine if one or several obstacles are present on the line segment $[p_i, p_j]$. If it is the case, the UAV can bypass the obstacles either by flying above them (path $[p_i \to q_1 \to q_2 \to p_j]$), or from a side (path $[p_i \to q_3 \to q_4 \to p_j]$), see Fig. 9b. The followed strategy is as follows, see Fig. 9c:

* If no obstacle is detected on $[p_i, p_j]$, the Euclidean distance $d_{free}(p_i, p_j)$ is calculated and entered in the cost matrix.

* If one or more obstacles are detected on $[p_i, p_j]$, the coordinates of the points $o_1$ and $o_2$ are first calculated. The required distance to bypass the obstacles by flying above them is then calculated:

$$d_{above}(p_i, p_j) = d(p_i, q_1) + d(q_1, q_2) + d(q_2, p_j) \quad (1)$$

Then, the cost by bypassing the obstacle from one side is explored. On the horizontal plan, several line segments parallel to $[o_1, o_2]$ are successively built on the right and left sides, with an increasing offset, see Fig. 9b. Once a free path is detected, the corresponding distance is calculated:

$$d_{side}(p_i, p_j) = d(p_i, q_3) + d(q_3, q_4) + d(q_4, p_j) \quad (2)$$

If $d_{side}(p_i, p_j) < d_{above}(p_i, p_j)$, it is preferable to bypass the obstacles from the side: $d_{side}(p_i, p_j)$ is entered in the cost matrix. Otherwise, if $d_{side}(p_i, p_j) > d_{above}(p_i, p_j)$, it is preferable to bypass the obstacles by flying above them. $d_{above}(p_i, p_j)$ is entered in the cost matrix. If $d_{side}(p_i, p_j) = d_{above}(p_i, p_j)$, the decision is to bypass the obstacles flying above them.

<figure><img src="image_11.png" /><figcaption>Fig. 9: a) Vertical view: a first way to bypass the obstacles between $[p_i, p_j]$ is to fly above them. $h_m$ is the maximal height of the obstacles. $h_s$ is the vertical safety distance imposed for the UAV (the horizontal safety distance has already been taken into account from the dilation operation in the image of obstacles), b) Horizontal view: a second way to bypass the obstacles between $[p_i, p_j]$ is to fly from a side, c) Flowchart of the strategy followed.</figcaption></figure>

## IV. RESOLUTION OF THE CE-TSP

The cost matrix is the essential element to solve the Traveling Salesman Problem (TSP), that means to find the optimal order for the UAV to visit the nodes. To find this optimal order, a Genetic Algorithm (GA), whose principle is presented in Fig. 10a, is used.

The principle is as follows: an initial population is created with $N$ possible routes for the UAV, randomly defined ($N$ must be a multiple of 5). This population is next divided in groups of 5 routes: in each group, the distance is calculated for each route (by using the cost matrix), and the route which has the minimal distance is kept and called the survivor. The other routes of the group are removed and replaced by copies of the survivor with mutations: on a random interval, the node orders are either reversed, swapped, right-shifted or left-shifted. Finally, all the routes are randomly remixed to form a new population. This process is repeated until a predetermined number of iterations is reached or the length of the best route is no more changing during several iterations. Next, from this optimal order, the possibility to modify the location of the target points inside the communication range of the nodes to reduce the trajectory length is considered. To that end, heuristic rules are applied to solve the CE-TSP, see the geometric considerations in Figs 10b and 10c.
<figure><img src="image_12.png" /><figcaption>Fig. 10: a) GA, see [12] for more details. N=1000, Iterations:5000, b) Heuristic rules after the GA. In the triangle ABC (location and range of three successive nodes), if the height $h_{BB'}$ is lower than the radius of the circle, B is replaced by $B'$, c) Otherwise, the bisecting line $BB'$ is calculated, and B is replaced by $B'$ which is the intersection point of the circle and the bisecting line. The trajectory to be followed by the UAV is therefore now AB'C.</figcaption></figure>

Finally, as the target points have slightly moved after the resolution of the CE-TSP, a verification step is applied to check if the new trajectory passes through an obstacle or not. If it is the case, the algorithm is again applied and the best solution kept (bypass the obstacles upwards, from the side, or previous trajectory before CE-TSP).

V. RESULTS

Fig. 11a first presents the output result of the GA applied on the use case of 100 nodes (i.e. resolution of the TSP based on the cost matrix including obstacles and bypass strategies). For the moment, this algorithm considers only the centers of the nodes. The total path distance is 9384.80 m. Fig. 11b presents the output results after the application of the heuristic rules (resolution of the CE-TSP). The trajectory now enters inside the communication range of the nodes, without the need to pass through the centers.

The total distance of this new trajectory is 8461.92 m (reduction of about 10 %). Only two obstacles are bypassed from the sides (the green crosses correspond to the points $q_3$ and $q_4$ in Fig. 9b), see the zoom in Fig 12a. Others obstacles are bypassed by flying above (blue crosses correspond to the points $q_1$ and $q_2$ in Fig. 9a). Fig. 12b and Fig. 13 display the results respectively in 3D and in the vertical plan. The maximal height of the UAV during this planned trajectory is 18.3 m, and 17 obstacles are bypassed by flying above them.

It is interesting to notice that if the UAV flies at the average speed of 5 m/s, it will take less than 29 minutes to carry out the path of 8461.91 m long: that is in line with the maximal time flight autonomy of our experimental UAV presented in Section III (40 minutes).

<figure><img src="image_13.png" /><figcaption>Fig. 11: a) Output of the Genetic Algorithm (TSP) : 9384.80 m, b) Output of the CE-TSP : 8461.92 m. The blue crosses correspond to the points $q_1$ and $q_2$ (bypass upwards). The green crosses correspond to the points $q_3$ and $q_4$ (bypass on the side).</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 12: a) Display of an obstacle bypassed on the side, b) View 3D of the planned trajectory</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Fig. 13: Height of the way-points for the UAV: the trajectory includes 17 vertical obstacles. The maximum value is 18.3 m.</figcaption></figure>

## VI. CONCLUSION

The Internet of Underground Things is going to play a crucial role in the years to come to monitor and manage various underground environments, such as soil conditions, water levels, and infrastructure health. However, the buried communicating sensor nodes are often confronted to very-short communication ranges, leading to accentuate their energy consumption to reach distant static gateways or other means of communication. A solution would be to come close to the nodes with a mobile data collector, that would limit the energy consumption spent by the nodes to communicate and, in addition, open the possibility to wake-up them by sending a suitable short-range signal.

This is the approach studied in this paper by considering a data collector embedded in a UAV flying at low height. However, the low flying heights combined with the necessity to optimize the path of the UAV in case of a large number of nodes to be visited require to have previously an accurate knowledge of the workspace of the UAV.

These problems have been addressed by exploiting the data of an airborne High Density LiDAR to create first the normalized Digital Surface Model and an image of obstacles. This information enabled to calculate the cost matrix in terms of Euclidean distances between the nodes. Two strategies have been considered in case one or more obstacles are present between two nodes: one calculates the distance to bypass the obstacles by flying above them, and the other calculates the distance to bypass the obstacles on the sides. Then, by applying a Genetic Algorithm completed with heuristic rules, the feasibility to accurately and optimally plan the path in 3D of the UAV to collect the data of 100 nodes distributed in our experimental farm was demonstrated. The final distance is moreover in line with the capacity of our drone in terms of speed and flight time autonomy.

To go further, we intend first to add the dynamics of the UAV in our models in order to have a path planning algorithm more precise and representative of a UAV's flight. Obviously, this trajectory is calculated off-line, and the UAV will have to adapt on-line to unforeseen situations (e.g. new obstacles). In this case, it would be interesting to add learning techniques to improve the trajectories of the UAV over time. Including heuristics before running the GA would be also an interesting way to improve the global path planning algorithm.

Finally, we are convincing that there are some great advantages to combine IoUT and UAV technologies, all the more in constrained environments, either due to unavailability of communication means or energy sources. This combination could largely contribute to sustainable development, resource management and disaster prevention, making the underground spaces more monitored and efficient.

## REFERENCES

[1] L. Moiroux-Arvis, C. Cariou, J.P. Chanet, Evaluation of LoRa technology in 433-MHz and 868-MHz for underground to aboveground data transmission. Computers and Electronics in Agriculture, 194, 2022, doi: 10.1016/j.compag.2022.106770

[2] C. Cariou, L. Moiroux-Arvis, F. Pinet, J.P. Chanet, Internet of Underground Things in Agriculture 4.0: Challenges, Applications and Perspectives, Sensors, 23(8), 4058, 2023, doi: 10.3390/s23084058

[3] A. Salam, U. Raza, Current advances in Internet of Underground Things. Signals in the soil, Springer Nature Switzerland AG, Chapter 10, 2020, doi: 10.1007/978-3-030-50861-6_10

[4] C. Ebi, F. Schaltegger, A. Rust, F. Blumensaat. Synchronous LoRa mesh network to monitor processes in underground infrastructure. IEEE Access, 7, 2019, doi: 10.1109/ACCESS.2019.2913985 2019.

[5] M. C. Vuran, A. Salam, R. Wong and S. Irmak, Internet of underground things: Sensing and communications on the field for precision agriculture, 4th World Forum on Internet of Things (WF-IoT), Singapore, 2018, pp. 586-591, doi: 10.1109/WF-IoT.2018.8355096

[6] A.R. Silva, M.C. Vuran, (CPS)2: integration of center pivot systems with wireless underground sensor networks for autonomous precision agriculture. ICCPS, Stockholm, Sweden, 2010.

[7] C.B.M. Ferreira, V.F. Peixoto, et al. UnderApp: a system for remote monitoring of landslides based on wireless underground sensor networks. WTIC, Rio de Janeiro, Brasil, 2019.

[8] I. Zaman, A. Forster, A. Challenges and opportunities of wireless underground sensor networks, 2018. doi: 10.13140/RG.2.2.20241.68968

[9] A.R. Da Silva, M. Moghaddam, M. Liu, M., The future of wireless underground sensing networks considering physical layer aspects. In: The Art of Wireless Sensor Networks, Signals and Communication Technology, 2014, doi: 10.1007/978-3-642-40066-7-12

[10] A. Augustin, T. Clausen, W.M. Townsley, A study of LoRa: long range and low power networks for the Internet of Things. Sensors 16, 1466, 2016, doi: 10.3390/s16091466

[11] M. Magno, V. Jelicic et al. Design, implementation, and performance evaluation of a flexible low-latency nanowatt wake-up radio receiver. IEEE Transactions on Industrial Informatics, 12(2), 2016.

[12] C. Cariou, L.M., F. Pinet, J.P. Chanet, Data collection from buried sensor nodes by means of an unmanned aerial vehicle. Sensors, 22(15), 5926, 2022, doi: 10.3390/s22155926

[13] M. Mozaffari, W. Saad, M. Bennis, Y.H. Nam, M. Debbah. A tutorial on UAVs for wireless networks: Applications, challenges, and open problems. IEEE Commun. Surv. Tutor. 2019, 21, 2334-2360

[14] W. Mennell, B. Golden, E. Wasil, A steiner zone heuristic for solving the Close Enough Traveling Salesman Problem, the 12th INFORMS Computing Society Conference, Homeland Defense, Monterey, CA, USA, 2011.

[15] S. Semami, H. Toulni, A. Elbyed. The Close Enough Traveling Salesman Problem with time window, Int. J. Circuits Syst. Signal Process. 13:579-584, 2019.

[16] W.P. Coutinho, R.Q. Nascimento, A.A. Pessoa, A. Subramanian, A branch-and-bound algorithm for the Close Enough Traveling Salesman Problem, Informs J. Comput., 28, 752-765, 2016. doi: 10.1287/ijoc.2016.0711

[17] C. Cariou, L. Moiroux-Arvis, F. Bendali, J. Mailfert. Optimal route planning of an Unmanned Aerial Vehicle for data collection of agricultural sensors, The 17th Int. Workshop on Networked Robotics and Communication Systems (NetRobiCS), Vancouver, Canada, 20-23 May, 2024