UAV swarm path planning with reinforcement learning for field prospecting

[Abstract] There has been steady growth in the adoption of Unmanned Aerial Vehicle (UAV) swarms by operators due to their time and cost benefits. However, this kind of system faces an important problem, which is the calculation of many optimal paths for each UAV. Solving this problem would allow control of many UAVs without human intervention while saving battery between recharges and performing several tasks simultaneously. The main aim is to develop a Reinforcement Learning based system capable of calculating the optimal flight path for a UAV swarm. This method stands out for its ability to learn through trial and error, allowing the model to adjust itself. The aim of these paths is to achieve full coverage of an overflight area for tasks such as field prospection, regardless of map size and the number of UAVs in the swarm. It is not necessary to establish targets or to have any previous knowledge other than the given map. Experiments have been conducted to determine whether it is optimal to establish a single control for all UAVs in the swarm or a control for each UAV. The results show that it is better to use one control for all UAVs because of the shorter flight time. In addition, the flight time is greatly affected by the size of the map. The results give starting points for future research, such as finding the optimal map size for each situation

Using Reinforcement Learning in the Path Planning of Swarms of UAVs for the Photographic Capture of Terrains

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main objective of this work is to design a system that, using Reinforcement Learning (RL) and Artificial Neural Networks (ANNs) techniques, can obtain a good path for each UAV in the swarm and distribute the flight environment in such a way that the combination of the captured images is as simple as possible. To determine whether it is better to use a global ANN or multiple local ANNs, experiments have been done over the same map and with different numbers of UAVs at different altitudes. The results are measured based on the time taken to find a solution. The results show that the system works with any number of UAVs if the map is correctly partitioned. On the other hand, using local ANNs seems to be the option that can find solutions faster, ensuring better trajectories than using a single global network. There is no need to use additional map information other than the current state of the environment, like targets or distance maps.This research received no external funding

A review of artificial intelligence applied to path planning in UAV swarms

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/ s00521-021-06569-4This is the accepted version of: A. Puente-Castro, D. Rivero, A. Pazos, and E. Fernández-Blanco, "A review of artificial intelligence applied to path planning in UAV swarms", Neural Computing and Applications, vol. 34, pp. 153–170, 2022. https://doi.org/10.1007/s00521-021-06569-4[Abstract]: Path Planning problems with Unmanned Aerial Vehicles (UAVs) are among the most studied knowledge areas in the related literature. However, few of them have been applied to groups of UAVs. The use of swarms allows to speed up the flight time and, thus, reducing the operational costs. When combined with Artificial Intelligence (AI) algorithms, a single system or operator can control all aircraft while optimal paths for each one can be computed. In order to introduce the current situation of these AI-based systems, a review of the most novel and relevant articles was carried out. This review was performed in two steps: first, a summary of the found articles; second, a quantitative analysis of the publications found based on different factors, such as the temporal evolution or the number of articles found based on different criteria. Therefore, this review provides not only a summary of the most recent work but it gives an overview of the trend in the use of AI algorithms in UAV swarms for Path Planning problems. The AI techniques of the articles found can be separated into four main groups based on their technique: reinforcement Learning techniques, Evolutive Computing techniques, Swarm Intelligence techniques, and, Graph Neural Networks. The final results show an increase in publications in recent years and that there is a change in the predominance of the most widely used techniques.This work is supported by Instituto de Salud Carlos III, grant number PI17/01826 (Collaborative Project in Genomic Data Integration (CICLOGEN) funded by the Instituto de Salud Carlos III from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe.”. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23). This work was also funded by the grant for the consolidation and structuring of competitive research units (ED431C 2018/49) from the General Directorate of Culture, Education and University Management of Xunta de Galicia, and the CYTED network (PCI2018_093284) funded by the Spanish Ministry of Ministry of Innovation and Science. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia “PRACTICUM DIRECT” Ref. IN845D-2020/03.Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Xunta de Galicia; IN845D-2020/0

Q-learning Based System for Path Planning with UAV Swarms in Obstacle Environments

Path Planning methods for autonomous control of Unmanned Aerial Vehicle (UAV) swarms are on the rise because of all the advantages they bring. There are more and more scenarios where autonomous control of multiple UAVs is required. Most of these scenarios present a large number of obstacles, such as power lines or trees. If all UAVs can be operated autonomously, personnel expenses can be decreased. In addition, if their flight paths are optimal, energy consumption is reduced. This ensures that more battery time is left for other operations. In this paper, a Reinforcement Learning based system is proposed for solving this problem in environments with obstacles by making use of Q-Learning. This method allows a model, in this particular case an Artificial Neural Network, to self-adjust by learning from its mistakes and achievements. Regardless of the size of the map or the number of UAVs in the swarm, the goal of these paths is to ensure complete coverage of an area with fixed obstacles for tasks, like field prospecting. Setting goals or having any prior information aside from the provided map is not required. For experimentation, five maps of different sizes with different obstacles were used. The experiments were performed with different number of UAVs. For the calculation of the results, the number of actions taken by all UAVs to complete the task in each experiment is taken into account. The lower the number of actions, the shorter the path and the lower the energy consumption. The results are satisfactory, showing that the system obtains solutions in fewer movements the more UAVs there are. For a better presentation, these results have been compared to another state-of-the-art approach

On the specificity of avian blood parasites: Revealing specific and generalist relationships between haemosporidians and biting midges

The study of host-parasite relationships involving vector-borne parasites requires understanding interactions between parasites and vectors. The capacity of haemosporidians to infect insects has clear evolutionary consequences for the transmission of diseases. Here, we investigated (i) the associations between blood parasites, biting midges and birds and (ii) the potential specificity between biting midge and haemosporidian haplotypes. A total of 629 parous biting midges Culicoides and 224 wild birds (belonging to seven species) from a locality of central Spain were individually examined for the presence of Haemoproteus and Plasmodium parasites by sequencing a fragment of cytochrome B. Biting midges were identified morphologically and characterized on the basis of a fragment of the cytochrome c oxidase (COI) gene. Overall, 12 Haemoproteus and three Plasmodium haplotypes were isolated and sequenced. Among them, 10 haplotypes were exclusively isolated from biting midges, three haplotypes only from birds and two haplotypes from both biting midges and birds. Biting midge haplotypes showed both specific and generalist relationships with Haemoproteus haplotypes but only generalist relationships with Plasmodium haplotypes. Several C. festivipennis and C. kibunesis haplotypes established significant coevolutionary links with Haemoproteus haplotypes. These results shed light on the specificity of interactions between vectors and blood parasites. © 2011 Blackwell Publishing Ltd.Peer Reviewe

Implementación de estrategias ludicas en la intervencion del bajo rendimiento academico de los estudiantes del grado 7° - 1 de la institucion educativa policarpa salavarrieta del municipio de Montería Cordoba.

En el presente proyecto se presenta una serie de actividades encaminadas a fortalecer las habilidades que conlleven al fortalecimiento del mejoramiento académico en los estudiantes del grado 7°- 1 de la Institución Educativa Policarpa Salavarrieta, en el estudiantes, docentes y padres de familia participan activamente de todos los talleres y orientaciones que para tal fin se implementan

Non-homogeneous dispersion of graphene in polyacrylonitrile substrates induces a migrastatic response and epithelial-like differentiation in MCF7 breast cancer cells

Background: Recent advances from studies of graphene and graphene-based derivatives have highlighted the great potential of these nanomaterials as migrastatic agents with the ability to modulate tumor microenvironments. Nevertheless, the administration of graphene nanomaterials in suspensions in vivo is controversial. As an alternative approach, herein, we report the immobilization of high concentrations of graphene nanoplatelets in polyacrylonitrile film substrates (named PAN/G10) and evaluate their potential use as migrastatic agents on cancer cells. Results: Breast cancer MCF7 cells cultured on PAN/G10 substrates presented features resembling mesenchymal-to-epithelial transition, e.g., (i) inhibition of migratory activity; (ii) activation of the expression of E-cadherin, cytokeratin 18, ZO-1 and EpCAM, four key molecular markers of epithelial differentiation; (iii) formation of adherens junctions with clustering and adhesion of cancer cells in aggregates or islets, and (iv) reorganization of the actin cytoskeleton resulting in a polygonal cell shape. Remarkably, assessment with Raman spectroscopy revealed that the above-mentioned events were produced when MCF7 cells were preferentially located on top of graphene-rich regions of the PAN/G10 substrates. Conclusions: The present data demonstrate the capacity of these composite substrates to induce an epithelial-like differentiation in MCF7 breast cancer cells, resulting in a migrastatic effect without any chemical agent-mediated signaling. Future works will aim to thoroughly evaluate the mechanisms of how PAN/G10 substrates trigger these responses in cancer cells and their potential use as antimetastatics for the treatment of solid cancers.This work was supported by Grants from the “Asociación Luchamos por la Vida” (Corrales de Buelna, Cantabria, Spain), Health Research Institute “Valdecilla” (INNVAL17/20, IDIVAL), MINECO/EIG-Concert Japan (X-MEM PCI2018-092929 project, International Joint Program 2018) and the Spanish Research Agency (PID2019-105827RB-I00 supported by MCIN/AEI/10.13039/501100011033)

Development and Validation of a New Clinical Scale for Infants with Acute Respiratory Infection: The ReSVinet Scale

Background and Aims A properly validated scoring system allowing objective categorization of infants with acute respiratory infections (ARIs), avoiding the need for in-person assessment and that could also be used by non-health professionals is currently not available. We aimed to develop a new clinical assessment scale meeting these specifications. Methods We designed a clinical scale (ReSVinet scale) based on seven parameters (feeding intolerance, medical intervention, respiratory difficulty, respiratory frequency, apnoea, general condition, fever) that were assigned different values (from 0 to 3) for a total of 20 points.170 children under two years of age with ARI were assessed independently by three pediatricians using this scale. Parents also evaluated their offspring with an adapted version of the scale in a subset of 61 cases. The scale was tested for internal consistency (Cronbach’s alpha), Pearson correlation coefficient for the items in the scale, inter-observer reliability (kappa index) and floor-ceiling effect. Results Internal consistency was good for all the observers, with the lowest Cronbach’s alpha being 0.72. There was a strong correlation between the investigators (r-value ranged 0.76–0.83) and also between the results obtained by the parents and the investigators(r = 0.73). Light’s kappa for the observations of the three investigators was 0.74. Weighted kappa in the group evaluated by the parents was 0.73. The final score was correlated with length of hospital stay, PICU admission and Wood-Downes Score. Conclusions The ReSVinet scale may be useful and reliable in the evaluation of infants with ARI, particularly acute bronchiolitis, even with data obtained from medical records and when employed by parents. Although further studies are necessary, ReSVinet scale already complies with more score validation criteria than the vast majority of the alternatives currently available and used in the clinical practiceThis work was supported by the Spanish Government (Research Program Health Research Fund – [http://www.gendres.org] (FIS; PI10/00540 y PI13/02382) National Plan I + D + I and FEDER funds) and Regional Galician funds (Promotion of Research Project 10 PXIB 918 184 PR) (FMT), and Ministerio de Ciencia e Innovación (SAF2011-26983) and the Plan Galego IDT, Xunta de Galicia (EM 2012/045) (AS). MC’s research activities have been supported by grants from Instituto de Investigación Sanitaria de Santiago de Compostela. FMT’s research activities have been supported by grants from Instituto Carlos III (Intensificación de la actividad investigadora- ISCIII/INT14/00245/ Cofinanciado FEDER), Spanish National Plan I + D + i and FEDER funds. Investigators received funding from the European Union’s Seventh Framework Program under ECGA no. 279185 (EUCLIDS – [http://www.euclids-project.eu]) during the production of this paper. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptS