From 74a9880eafecb3343e92826443e1553431546dbc Mon Sep 17 00:00:00 2001 From: csanrod Date: Fri, 15 Sep 2023 12:59:52 +0200 Subject: [PATCH] added some corrections to chaper 1 in the memory, and also added vectors into VFF --- memoria/bibliografia.bib | 19 ++ memoria/capitulos/capitulo1.tex | 23 +- memoria/imagenes/cap1/1-5_real_drone.jpeg | Bin 0 -> 94092 bytes memoria/memoria.aux | 92 ++++---- memoria/memoria.bbl | 13 ++ memoria/memoria.blg | 134 +++++------ memoria/memoria.lof | 1 + memoria/memoria.log | 259 ++++++++++++---------- memoria/memoria.pdf | Bin 4643005 -> 4738419 bytes memoria/portada/resumen.tex | 2 +- src/teleop/scripts/algorithms.py | 38 +++- 11 files changed, 342 insertions(+), 239 deletions(-) create mode 100644 memoria/imagenes/cap1/1-5_real_drone.jpeg diff --git a/memoria/bibliografia.bib b/memoria/bibliografia.bib index 32c9811c..614b0cf1 100644 --- a/memoria/bibliografia.bib +++ b/memoria/bibliografia.bib @@ -61,6 +61,25 @@ @Inbook{Wang2022 url="https://doi.org/10.1007/978-981-19-1983-1_7" } +@Electronic{agv-vs-amr, + Title = {\textit{AMR vs AGV - What's the difference?}}, + howpublished= {\url{https://www.mobile-industrial-robots.com/insights/get-started-with-amrs/agv-vs-amr-whats-the-difference/}}, + author = {MiR}, + year = {2023} +} + +@article{10.3389/fmech.2020.00011, + author={Maiorino, Andrea and Muscolo, Giovanni Gerardo}, + title={Biped Robots With Compliant Joints for Walking and Running Performance Growing}, + journal={Frontiers in Mechanical Engineering}, + volume={6}, + year={2020}, + url={https://www.frontiersin.org/articles/10.3389/fmech.2020.00011}, + doi={10.3389/fmech.2020.00011}, + issn={2297-3079}, + abstract={This paper deals with the problem of compliance in biped robots locomotion. After a first literature review, we designed and simulated a 3D virtual model of a biped robot conceived with the same link dimensions (and weights) of a standard man. In all simulation, the same input to the robot actuators are proposed, modifying only the compliance of the attached links. We first validated the model and then compared results increasing and reducing compliance on hip, knee and ankle joints. The very good results underline how the robot performances may be increased including the compliant element in the knee. In particular, we noted how including a proper value of the compliance in the knee, power and torque of the actuators may be reduced increasing robot speed.} +} + @Electronic{kettering-bug, Title = {\textit{Kettering Aerial Torpedo “Bug”}}, howpublished= {\url{https://www.nationalmuseum.af.mil/Visit/Museum-Exhibits/Fact-Sheets/Display/Article/198095/kettering-aerial-torpedo-bug/}}, diff --git a/memoria/capitulos/capitulo1.tex b/memoria/capitulos/capitulo1.tex index 344aa85f..b3a5e072 100644 --- a/memoria/capitulos/capitulo1.tex +++ b/memoria/capitulos/capitulo1.tex @@ -11,21 +11,23 @@ \chapter{Introducción} \begin{enumerate} \item \textbf{Robótica industrial}: que involucra mecanismos fijos, capaces de realizar tareas de manera rápida, precisa y eficiente. Como es el caso de los brazos robóticos \cite{industrial-robot}. - \item \textbf{Robótica móvil}: la cual abarca el resto de dispositivos no mencionados, que engloba múltiples entornos y aplicaciones, como pueden ser, robótica aérea, terrestre y submarina \cite{mobile-robot}. + \item \textbf{Robótica móvil}: la cual abarca a los dispositivos móviles que se engloban en múltiples entornos y aplicaciones, como pueden ser, robótica aérea, terrestre y submarina \cite{mobile-robot}. \end{enumerate} \begin{figure} [h] \centering - \subfigure[Robot industrial ABB]{\includegraphics[height=4cm]{imagenes/cap1/1_industrial_robot.jpeg}} + \subfigure[Robot industrial ABB]{\includegraphics[height=3cm]{imagenes/cap1/1_industrial_robot.jpeg}} \quad - \subfigure[Robot móvil Spot]{\includegraphics[height=4cm]{imagenes/cap1/2_mobile_robot.jpeg}} + \subfigure[Robot móvil Spot]{\includegraphics[height=3cm]{imagenes/cap1/2_mobile_robot.jpeg}} + \quad + \subfigure[Robot móvil aéreo]{\includegraphics[height=3cm]{imagenes/cap1/1-5_real_drone.jpeg}} \caption{Robótica industrial VS robótica móvil} \label{fig:industrial_vs_mobile} \end{figure} -Como tal, la robótica es especialmente buena a la hora de resolver tareas repetitivas, peligrosas, delicadas y en ambientes problemáticos (conocidas como las 4 D's, \emph{dull, dirty, dangerous and dear}) \cite{4-d}. Sin embargo, uno de los problemas más complicados de abordar, es el \textbf{contexto}, es decir, la capacidad de entender y adaptarse a las circunstancias del problema. Es ahí, donde se presenta el segundo gran protagonista, la \ac{IA} \cite{dworakowski2020robots}.\\ +Como tal, la robótica ayuda a resolver tareas repetitivas, peligrosas, delicadas y en ambientes problemáticos (conocidas como las 4 D's, \emph{dull, dirty, dangerous and dear}) \cite{4-d}. Sin embargo, uno de los problemas más complicados de abordar, es el \textbf{contexto}, es decir, la capacidad de entender y adaptarse a las circunstancias del problema, como por ejemplo en el caso de la conducción autónoma, donde detectar un simple peatón, puede derivar en infinitos inconvenientes (condiciones de visibilidad, clima, atuendo, entre muchos otros). Es ahí, donde se presenta el segundo gran protagonista, la \ac{IA} \cite{dworakowski2020robots}.\\ -Pero antes de entrar en materia, primero hay que definir los conceptos básicos uno a uno, como: ¿qué es exactamente un robot? ¿qué tipo de robot usaremos? ¿en qué consiste la \ac{IA} y que emplearemos? ¿cómo funcionan las señales que rastrearemos? entre otras cuestiones. +A continuación se definirán conceptos básicos como: ¿qué es exactamente un robot? ¿qué tipo de robot usaremos? ¿en qué consiste la \ac{IA} y que emplearemos? ¿cómo funcionan las señales que rastrearemos? entre otras cuestiones. \section{Robots} \label{sec:robots} @@ -40,9 +42,9 @@ \section{Robots} \label{fig:robot_def} \end{figure} -Según el problema que se quiera resolver conviene usar unos u otros. En nuestro caso buscamos un robot con capacidad de navegar, preferiblemente grandes distancias y que pueda tomar medidas de la intensidad de una señal. De este modo, para el primer punto se tenían dos opciones, o bien un robot terrestre, o bien un robot aéreo. Para el segundo punto no influye ya que se podía incluir el sensor en cualquier dispositivo.\\ +Según el problema que se quiera resolver conviene usar unos u otros. En nuestro caso buscamos un robot con capacidad de navegar, preferiblemente grandes distancias y que pueda tomar medidas de la intensidad de una señal de forma autónoma.\\ -Finalmente, y dadas las características del problema, se opta por la solución aérea, ya que permite barrer grandes superficies sin depender de la orografía terreno. +Existen múltiples robots capaces de satisfacer estas condiciones, vease, los AGV/AMR o plataformas robóticas terrestres ampliamente empleadas logística \cite{agv-vs-amr}; robots bipedos, los cuales emulan el movimiento humanoide, lo que aumenta su adaptabilidad a cualquier entorno real (ya que el mundo esta diseñado para la biomecánica humana), sin embargo, es bastante complejo replicar la marcha bípeda \cite{10.3389/fmech.2020.00011}; y por último, drones, las cuales detallaremos a continuación. \subsection{Drones} \label{subsec:drones} @@ -60,7 +62,6 @@ \subsection{Drones} \label{fig:drone_history} \end{figure} -\newpage Existen múltiples avances y ejemplos posteriores, pero en la actualidad podemos definir un \textbf{\ac{UAS}} teniendo en cuenta lo siguiente: \begin{enumerate} @@ -83,17 +84,19 @@ \subsection{Drones} Tal y como fue mencionado, la gran ventaja del uso de vehículos aéreos es poder evitar las irregularidades del terreno, sin embargo, hay ligados al uso de estos dispositivos ciertos problemas, como son el clima, la carga de pago que afecta a la autonomía (peso de las baterías), los interiores (afectan a la señal GPS), entre otros. Esto, se debe tener en cuenta de cara a la resolución del problema.\\ +Agrupando la robótica y los drones, se pueden observar múltiples ejemplos de uso, uno muy conocido es el de un dron \emph{``sigue-persona''}, el cual permite a un \ac{SUAV} detectar y moverse al son de un objetivo móvil, tal y como puede ser una persona. Todo esto en síntesis con técnicas de \ac{IA}, la cual introduciremos a continuación. + \section{Inteligencia artificial} \label{subsec:inteligencia_artificial} -El segundo pilar mencionado en este \ac{TFG}, es el de la \ac{IA}, pero, ¿de dónde surge esto?. Quizás, la primera pregunta que se buscó responder fue la siguiente \textbf{¿Puede una máquina pensar?}, formulada en \emph{``Computing Machinery and Intelligence''} (Alan Turing, 1950), de donde surgió el famoso test de Turing, entre otras ideas \cite{turing-paper}. La búsqueda de la \ac{IA} enfrentó desafíos iniciales debido a la incapacidad de las primeras computadoras para almacenar datos y su elevado precio. Sin embargo, en 1956, se presentó el primer programa de \ac{IA} llamado \textbf{Logic Theorist} en el \textbf{Dartmouth Summer Research Project on Artificial Intelligence} \cite{logic-theorist}. Con el tiempo, la IA progresó con mejores algoritmos y mejoras en la capacidad de las computadoras. A pesar de esto, lograr los objetivos finales de la IA, como comprender el lenguaje humano y el pensamiento abstracto, sigue siendo un desafío a día de hoy \cite{history-ai}.\\ +La \ac{IA} ha tenido un auge importante en los últimos años, especialmente en el ámbito de la robótica dado su amplio abanico de soluciones sinérgicas con la misma, sin embargo, conviene conocer lo origenes. Quizás, la primera pregunta que se buscó responder fue la siguiente \textbf{¿Puede una máquina pensar?}, formulada en \emph{``Computing Machinery and Intelligence''} (Alan Turing, 1950), de donde surgió el famoso test de Turing, entre otras ideas \cite{turing-paper}. La búsqueda de la \ac{IA} enfrentó desafíos iniciales debido a la incapacidad de las primeras computadoras para almacenar datos y su elevado precio. Sin embargo, en 1956, se presentó el primer programa de \ac{IA} llamado \textbf{Logic Theorist} en el \textbf{Dartmouth Summer Research Project on Artificial Intelligence} \cite{logic-theorist}. Con el tiempo, la IA progresó con mejores algoritmos y mejoras en la capacidad de las computadoras. A pesar de esto, lograr los objetivos finales de la IA, como comprender el lenguaje humano y el pensamiento abstracto, sigue siendo un desafío a día de hoy \cite{history-ai}.\\ En general, la \ac{IA} es capaz de abordar los siguientes problemas: \begin{enumerate} \item \textbf{Predicción}: que busca adelantar una respuesta precisa, con ciertos datos de entrada. Por ejemplo, predecir el precio de una vivienda en función de sus metros cuadrados. - \item \textbf{Clasificación}: que engloba datos en grupos según sus características. Como puede ser, detectar rostros de personas en imagenes. + \item \textbf{Clasificación}: que engloba datos en grupos según sus características. Como puede ser, detectar rostros de personas en imágenes. \item \textbf{Aprendizaje}: Que busca resolver un problema a base de prueba y error, mediante un sistema de recompensas. 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\newacro{RAE}[\AC@hyperlink{RAE}{RAE}]{Real Academia Española} \newacro{TFG}[\AC@hyperlink{TFG}{TFG}]{Trabajo de Fin de Grado} \newacro{UAV}[\AC@hyperlink{UAV}{UAV}]{\emph {Unmanned Air Vehicles}} @@ -49,6 +46,8 @@ \newacro{RSSI}[\AC@hyperlink{RSSI}{RSSI}]{\emph {Received Signal Strength Indicator}} \newacro{SNR}[\AC@hyperlink{SNR}{SNR}]{\emph {Signal to Noise Ratio}} \newacro{PLE}[\AC@hyperlink{PLE}{PLE}]{\emph {Path-Loss Exponent}} +\AC@undonewlabel{acro:ROS} +\newlabel{acro:ROS}{{}{\es@scroman {iv}}{\contentsname \@mkboth {\MakeUppercase \contentsname }{\MakeUppercase \contentsname }}{section*.8}{}} \acronymused{ROS} \citation{rae-robotica} \citation{revista-de-robots} @@ -71,6 +70,7 @@ \newlabel{fig:industrial_vs_mobile}{{1.1}{2}{Robótica industrial VS robótica móvil\relax }{figure.caption.13}{}} \@writefile{lof}{\contentsline {subfigure}{\numberline{(a)}{\ignorespaces {Robot industrial ABB}}}{2}{figure.caption.13}\protected@file@percent } \@writefile{lof}{\contentsline {subfigure}{\numberline{(b)}{\ignorespaces {Robot móvil Spot}}}{2}{figure.caption.13}\protected@file@percent } +\@writefile{lof}{\contentsline {subfigure}{\numberline{(c)}{\ignorespaces {Robot móvil aéreo}}}{2}{figure.caption.13}\protected@file@percent } \AC@undonewlabel{acro:IA} \newlabel{acro:IA}{{1}{2}{Introducción}{section*.14}{}} \acronymused{IA} @@ -79,6 +79,8 @@ \newlabel{sec:robots}{{1.1}{2}{Robots}{section.1.1}{}} \@writefile{lof}{\contentsline {figure}{\numberline {1.2}{\ignorespaces Definición de robot}}{2}{figure.caption.15}\protected@file@percent } \newlabel{fig:robot_def}{{1.2}{2}{Definición de robot}{figure.caption.15}{}} +\citation{agv-vs-amr} +\citation{10.3389/fmech.2020.00011} \citation{kettering-bug} \citation{queen-bee} \citation{operation-aphrodite} @@ -408,46 +410,48 @@ \bibcite{4-d}{{5}{}{{}}{{}}} \bibcite{dworakowski2020robots}{{6}{}{{}}{{}}} \bibcite{Wang2022}{{7}{}{{}}{{}}} -\bibcite{kettering-bug}{{8}{}{{}}{{}}} -\bibcite{queen-bee}{{9}{}{{}}{{}}} -\bibcite{operation-aphrodite}{{10}{}{{}}{{}}} +\bibcite{agv-vs-amr}{{8}{}{{}}{{}}} +\bibcite{10.3389/fmech.2020.00011}{{9}{}{{}}{{}}} +\bibcite{kettering-bug}{{10}{}{{}}{{}}} \@writefile{toc}{\contentsline {chapter}{Bibliograf\'{\i }a}{50}{appendix*.73}\protected@file@percent } -\bibcite{uas-definicion}{{11}{}{{}}{{}}} -\bibcite{gcs-definicion}{{12}{}{{}}{{}}} -\bibcite{data-link-definicion}{{13}{}{{}}{{}}} -\bibcite{drone-regulation}{{14}{}{{}}{{}}} -\bibcite{turing-paper}{{15}{}{{}}{{}}} -\bibcite{logic-theorist}{{16}{}{{}}{{}}} -\bibcite{history-ai}{{17}{}{{}}{{}}} -\bibcite{reg-class}{{18}{}{{}}{{}}} -\bibcite{learn}{{19}{}{{}}{{}}} -\bibcite{psicologia-aprendizaje}{{20}{}{{}}{{}}} -\bibcite{skinner}{{21}{}{{}}{{}}} -\bibcite{q-learning}{{22}{}{{}}{{}}} -\bibcite{friis-1}{{23}{}{{}}{{}}} -\bibcite{friis-2}{{24}{}{{}}{{}}} -\bibcite{friis-3}{{25}{}{{}}{{}}} -\bibcite{tfm-pedro}{{26}{}{{}}{{}}} -\bibcite{paper-ia-dron}{{27}{}{{}}{{}}} -\bibcite{tiobe}{{28}{}{{}}{{}}} -\bibcite{python-history}{{29}{}{{}}{{}}} -\bibcite{python-def}{{30}{}{{}}{{}}} -\bibcite{compiled-vs-interpreted}{{31}{}{{}}{{}}} -\bibcite{c-history}{{32}{}{{}}{{}}} -\bibcite{middleware-def}{{33}{}{{}}{{}}} -\bibcite{ros-def}{{34}{}{{}}{{}}} -\bibcite{gazebo-def}{{35}{}{{}}{{}}} -\bibcite{rviz-def}{{36}{}{{}}{{}}} -\bibcite{vscode-def}{{37}{}{{}}{{}}} -\bibcite{github-def}{{38}{}{{}}{{}}} -\bibcite{opencv-def}{{39}{}{{}}{{}}} -\bibcite{matplotlib-def}{{40}{}{{}}{{}}} -\bibcite{flight-controller}{{41}{}{{}}{{}}} -\bibcite{px4-def}{{42}{}{{}}{{}}} -\bibcite{px4-mavlink}{{43}{}{{}}{{}}} -\bibcite{px4-mavros}{{44}{}{{}}{{}}} -\bibcite{jderobot-ref}{{45}{}{{}}{{}}} -\bibcite{flight-modes}{{46}{}{{}}{{}}} -\bibcite{basics-signals}{{47}{}{{}}{{}}} -\bibcite{poly-info}{{48}{}{{}}{{}}} +\bibcite{queen-bee}{{11}{}{{}}{{}}} +\bibcite{operation-aphrodite}{{12}{}{{}}{{}}} +\bibcite{uas-definicion}{{13}{}{{}}{{}}} +\bibcite{gcs-definicion}{{14}{}{{}}{{}}} +\bibcite{data-link-definicion}{{15}{}{{}}{{}}} +\bibcite{drone-regulation}{{16}{}{{}}{{}}} +\bibcite{turing-paper}{{17}{}{{}}{{}}} +\bibcite{logic-theorist}{{18}{}{{}}{{}}} +\bibcite{history-ai}{{19}{}{{}}{{}}} +\bibcite{reg-class}{{20}{}{{}}{{}}} +\bibcite{learn}{{21}{}{{}}{{}}} +\bibcite{psicologia-aprendizaje}{{22}{}{{}}{{}}} +\bibcite{skinner}{{23}{}{{}}{{}}} +\bibcite{q-learning}{{24}{}{{}}{{}}} +\bibcite{friis-1}{{25}{}{{}}{{}}} +\bibcite{friis-2}{{26}{}{{}}{{}}} +\bibcite{friis-3}{{27}{}{{}}{{}}} +\bibcite{tfm-pedro}{{28}{}{{}}{{}}} +\bibcite{paper-ia-dron}{{29}{}{{}}{{}}} +\bibcite{tiobe}{{30}{}{{}}{{}}} +\bibcite{python-history}{{31}{}{{}}{{}}} +\bibcite{python-def}{{32}{}{{}}{{}}} +\bibcite{compiled-vs-interpreted}{{33}{}{{}}{{}}} +\bibcite{c-history}{{34}{}{{}}{{}}} +\bibcite{middleware-def}{{35}{}{{}}{{}}} +\bibcite{ros-def}{{36}{}{{}}{{}}} +\bibcite{gazebo-def}{{37}{}{{}}{{}}} +\bibcite{rviz-def}{{38}{}{{}}{{}}} +\bibcite{vscode-def}{{39}{}{{}}{{}}} +\bibcite{github-def}{{40}{}{{}}{{}}} +\bibcite{opencv-def}{{41}{}{{}}{{}}} +\bibcite{matplotlib-def}{{42}{}{{}}{{}}} +\bibcite{flight-controller}{{43}{}{{}}{{}}} +\bibcite{px4-def}{{44}{}{{}}{{}}} +\bibcite{px4-mavlink}{{45}{}{{}}{{}}} +\bibcite{px4-mavros}{{46}{}{{}}{{}}} +\bibcite{jderobot-ref}{{47}{}{{}}{{}}} +\bibcite{flight-modes}{{48}{}{{}}{{}}} +\bibcite{basics-signals}{{49}{}{{}}{{}}} +\bibcite{poly-info}{{50}{}{{}}{{}}} \providecommand\NAT@force@numbers{}\NAT@force@numbers diff --git a/memoria/memoria.bbl b/memoria/memoria.bbl index ba8a1f52..dfbf507a 100644 --- a/memoria/memoria.bbl +++ b/memoria/memoria.bbl @@ -40,6 +40,19 @@ Jiefei Wang and Damith Herath. 177--203. \newblock Springer Nature Singapore, Singapore, 2022. +\bibitem{agv-vs-amr} +MiR. +\newblock \textit{AMR vs AGV - What's the difference?} +\newblock + \url{https://www.mobile-industrial-robots.com/insights/get-started-with-amrs/agv-vs-amr-whats-the-difference/}, + 2023. + +\bibitem{10.3389/fmech.2020.00011} +Andrea Maiorino and Giovanni~Gerardo Muscolo. +\newblock Biped robots with compliant joints for walking and running + performance growing. +\newblock {\em Frontiers in Mechanical Engineering}, 6, 2020. + \bibitem{kettering-bug} National~Museum of~the United States Air~Force. \newblock \textit{Kettering Aerial Torpedo “Bug”}. diff --git a/memoria/memoria.blg b/memoria/memoria.blg index f279cfd9..b12496be 100644 --- a/memoria/memoria.blg +++ b/memoria/memoria.blg @@ -13,129 +13,131 @@ Warning--entry type for "mobile-robot" isn't style-file defined --line 24 of file bibliografia.bib Warning--entry type for "4-d" isn't style-file defined --line 31 of file bibliografia.bib -Warning--entry type for "kettering-bug" isn't style-file defined +Warning--entry type for "agv-vs-amr" isn't style-file defined --line 64 of file bibliografia.bib +Warning--entry type for "kettering-bug" isn't style-file defined +--line 83 of file bibliografia.bib Warning--entry type for "queen-bee" isn't style-file 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Package atveryend Info: Empty hook `AtVeryVeryEnd' on input line 60. ) Here is how much of TeX's memory you used: - 14192 strings out of 483048 - 209853 string characters out of 5962772 + 14208 strings out of 483048 + 210279 string characters out of 5962772 663405 words of memory out of 5000000 - 28464 multiletter control sequences out of 15000+600000 + 28476 multiletter control sequences out of 15000+600000 567891 words of font info for 81 fonts, out of 8000000 for 9000 91 hyphenation exceptions out of 8191 44i,12n,53p,1069b,1467s stack positions out of 5000i,500n,10000p,200000b,80000s @@ -2210,10 +2239,10 @@ ive/texmf-dist/fonts/type1/public/amsfonts/cm/cmsy6.pfb> -Output written on memoria.pdf (63 pages, 4643005 bytes). +Output written on memoria.pdf (63 pages, 4738419 bytes). 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zg2d#(%0a1$u-6LfWx|yNXs{5DgV^N}Qiz~=_CSLkfn*y})e3u`Le-1FjYGNM_vo@9 vk3&B^I>PsoE&a^w%e&q7t^0%?S)sm3m6n$xMC4-UW@Y1oqofp9lz{s`ej4$w diff --git a/memoria/portada/resumen.tex b/memoria/portada/resumen.tex index 67a9263c..36e19ce3 100644 --- a/memoria/portada/resumen.tex +++ b/memoria/portada/resumen.tex @@ -8,4 +8,4 @@ \chapter*{Resumen\markboth{Resumen}{Resumen}} De este modo, surge la idea de realizar este \ac{TFG}, juntando lo mejor de ambos mundos, soluciones autónomas con dispositivos aéreos tremendamente adaptables a las circunstancias del problema.\\ -Concretamente, el objetivo de este proyecto ha sido robotizar un dron, con el fin de rastrear y navegar una señal de \ac{RF}, mediante el uso de distintos algoritmos, dentro de la infraestructura proporcionada por \ac{ROS}, con el fin de realizar una comparativa y determinar cual desempeña mejor su papel. \ No newline at end of file +Concretamente, el objetivo de este proyecto ha sido demostrar que, empleando aprendizaje por refuerzo (Q-Learning), se puede lograr rastrear y navegar hacia una señal \ac{RF} de forma más efectiva que con el resto de aproximaciones planteadas. \ No newline at end of file diff --git a/src/teleop/scripts/algorithms.py b/src/teleop/scripts/algorithms.py index 5b0099f1..6c812014 100644 --- a/src/teleop/scripts/algorithms.py +++ b/src/teleop/scripts/algorithms.py @@ -1354,6 +1354,37 @@ def get_distance_to_goal(self): return d + def get_obs_resultant(self): + current_pose = rospy.wait_for_message(LOCAL_POSE_TOPIC, PoseStamped) + + x = current_pose.pose.position.x + y = current_pose.pose.position.y + + x_res = 0.0 + y_res = 0.0 + + counter = 0 + + for obs_coord in OBSTACLE_COORDS: + x_obs, y_obs = self.heatmapcoords_to_gzcoords(obs_coord) + x_res += x - x_obs + y_res += y - y_obs + counter += 1 + + return (x_res/counter, y_res/counter) + + def get_goal_vec(self): + current_pose = rospy.wait_for_message(LOCAL_POSE_TOPIC, PoseStamped) + + x = current_pose.pose.position.x + y = current_pose.pose.position.y + + x_goal, y_goal = self.heatmapcoords_to_gzcoords(SIGNAL_ORIGIN) + + return (x_goal - x, y_goal - y) + + + def VFF(self): goal_pose = PoseStamped() goal_pose.pose.position.z = H_POSES @@ -1367,9 +1398,10 @@ def VFF(self): while True: self.vel_pub.publish(vel) - print(self.get_distance_to_obstacle()) - print(self.get_distance_to_goal()) - print(self.get_distance_to_obstacle(minimun=True)) + print((self.get_goal_vec(), self.get_obs_resultant())) + # print(self.get_distance_to_obstacle()) + # print(self.get_distance_to_goal()) + # print(self.get_distance_to_obstacle(minimun=True))