2st Award of Innovative Solution
Panhellenic Educational Robotics Competition FLL 2023 | March 2023
Thermoelectric Generators
Panhellenic Educational Robotics Competition FLL 2023 | March 2023
Thermoelectric Generators
Idea origin
In the Mediterranean countries, as in our country, the use of solar water heaters is quite extensive, both in houses and in hotel units. Especially in the summer, those of us who have a solar water heater in our home will know that solar water is much hotter than we need. Moreover, even if we consume all the hot water, the replacement of it becomes almost immediate, as the temperatures developed by the sun in the summer season are substantial. The question, therefore, is if there is a way to utilize this excess hot water in the field of energy production and storage. How, therefore, can we harness this heat, which will anyway be discarded to the environment?
Thermoelectric Generators (Thermoelectric Generator - TEC)
The thermoelectric generator converts the temperature difference into electricity based on various phenomena, including the Peltier effect.
Idea description
The solar water heater consists of two parts, the collection part, i.e. the solar panels on the absorption surface of solar radiation and the storage section, i.e. the boiler. The water circulating in the solar panels collectors are much hotter than that of the tank, so if with it power our system, the current we will produce will be much higher strength. The idea is to build a system in which the hot water of the solar water heater will be continuously recycled so that using thermoelectric plates produces electricity. This energy will be used for the power supply devices that use direct current, for example, could mobile phone or portable device charging stations are powered computers in homes and hotel units, and why not, even stations charging cars that will overwhelm the market in the coming years. This system will be autonomous and easy to install in any solar water heater that consumers already have to limit production costs and purchase new systems.
This system will be autonomous and easy to install in any solar water heater that consumers already have to limit production costs and purchase new systems. In addition, it will mediate between the water supply network, so only a simple plumbing installation will be required for the solar water heater.
In the Mediterranean countries, as in our country, the use of solar water heaters is quite extensive, both in houses and in hotel units. Especially in the summer, those of us who have a solar water heater in our home will know that solar water is much hotter than we need. Moreover, even if we consume all the hot water, the replacement of it becomes almost immediate, as the temperatures developed by the sun in the summer season are substantial. The question, therefore, is if there is a way to utilize this excess hot water in the field of energy production and storage. How, therefore, can we harness this heat, which will anyway be discarded to the environment?
Thermoelectric Generators (Thermoelectric Generator - TEC)
The thermoelectric generator converts the temperature difference into electricity based on various phenomena, including the Peltier effect.
Idea description
The solar water heater consists of two parts, the collection part, i.e. the solar panels on the absorption surface of solar radiation and the storage section, i.e. the boiler. The water circulating in the solar panels collectors are much hotter than that of the tank, so if with it power our system, the current we will produce will be much higher strength. The idea is to build a system in which the hot water of the solar water heater will be continuously recycled so that using thermoelectric plates produces electricity. This energy will be used for the power supply devices that use direct current, for example, could mobile phone or portable device charging stations are powered computers in homes and hotel units, and why not, even stations charging cars that will overwhelm the market in the coming years. This system will be autonomous and easy to install in any solar water heater that consumers already have to limit production costs and purchase new systems.
This system will be autonomous and easy to install in any solar water heater that consumers already have to limit production costs and purchase new systems. In addition, it will mediate between the water supply network, so only a simple plumbing installation will be required for the solar water heater.
Mission Space Lab Phase 4
Life on Earth | 2023
Introduction
The importance of studying and analysing historical data in satellite images of our planet is well-known in many scientific fields. Especially in the process of studying the effects on our planet due to climate change, it has offered and will continue to offer unique opportunities to prevent and solve problems before they become "irreversible".
The goal of our experiment is not to study a specific phenomenon, but rather focuses on improving the tools a scientific team uses to study the evolution of specific phenomena that occur on the surface of our planet over time. For the needs of our experiment, we chose to develop and utilize an application that will help us study the change in the NDVI (vegetation index) in specific regions.
The user using our application will be able to easily select the images of interest and by utilizing data such as geographic coordinates and reference years that they wish to study, our application will extract the necessary data and make it available to the researcher, with an easily manageable interface and many customization options.
Method
The initial approach was to develop a "standalone" Python application to analyse NDVI of specific regions from images collected by Mark II and then retrieve and present similar historical data.
The first stage of data collection from the ISS was successfully completed. We collected 458 images with their geographic coordinates, of which about 300 were "daytime" images (for safety reasons we allowed some "nighttime" images). Because we needed images of land areas to do our analysis, we selected 5 images, without excessive cloud cover and with sufficient land area.
Next challenge was the "automated" search for older photos of the areas we were studying. We were greatly aided in this process by Google Earth Engine (EE), with the satellite data libraries, as well as the tools available from the EE Python api (NDVI calculations, sequential resolution levels, etc.).
Our final project was developed in Google Colab and is available on github. This choice helped us to make use of Google’s tools more easily, which we needed anyway to complete our project, but also to integrate into it our own scripts we had developed for NDVI analysis of the images collected from the ISS.
Conclusion
Although we feel that our idea has been implemented to a good percentage, the result certainly differs in many ways from our original plan. In the beginning we didn't even think that we would use the tools we finally chose, and that's because none of us had worked with Google Earth Engine (EE).
Finally, we were able to create a "tool" that can be used even by a researcher who doesn't know programming to gather useful historical satellite data and compare it with current data quickly and easily. Certainly, the project is open to many improvements, such as:
The importance of studying and analysing historical data in satellite images of our planet is well-known in many scientific fields. Especially in the process of studying the effects on our planet due to climate change, it has offered and will continue to offer unique opportunities to prevent and solve problems before they become "irreversible".
The goal of our experiment is not to study a specific phenomenon, but rather focuses on improving the tools a scientific team uses to study the evolution of specific phenomena that occur on the surface of our planet over time. For the needs of our experiment, we chose to develop and utilize an application that will help us study the change in the NDVI (vegetation index) in specific regions.
The user using our application will be able to easily select the images of interest and by utilizing data such as geographic coordinates and reference years that they wish to study, our application will extract the necessary data and make it available to the researcher, with an easily manageable interface and many customization options.
Method
The initial approach was to develop a "standalone" Python application to analyse NDVI of specific regions from images collected by Mark II and then retrieve and present similar historical data.
The first stage of data collection from the ISS was successfully completed. We collected 458 images with their geographic coordinates, of which about 300 were "daytime" images (for safety reasons we allowed some "nighttime" images). Because we needed images of land areas to do our analysis, we selected 5 images, without excessive cloud cover and with sufficient land area.
Next challenge was the "automated" search for older photos of the areas we were studying. We were greatly aided in this process by Google Earth Engine (EE), with the satellite data libraries, as well as the tools available from the EE Python api (NDVI calculations, sequential resolution levels, etc.).
Our final project was developed in Google Colab and is available on github. This choice helped us to make use of Google’s tools more easily, which we needed anyway to complete our project, but also to integrate into it our own scripts we had developed for NDVI analysis of the images collected from the ISS.
Conclusion
Although we feel that our idea has been implemented to a good percentage, the result certainly differs in many ways from our original plan. In the beginning we didn't even think that we would use the tools we finally chose, and that's because none of us had worked with Google Earth Engine (EE).
Finally, we were able to create a "tool" that can be used even by a researcher who doesn't know programming to gather useful historical satellite data and compare it with current data quickly and easily. Certainly, the project is open to many improvements, such as:
- The colour palette we use in the EE compared to the one we use in our scripts does not lead to the same visual effect. This is possibly because in our camera a red filter was used, and the infrared filter was removed, so we need to find the palette that will correct this difference.
- The user cannot easily isolate in the original image a specific part of the image and restrict the measurement there, this must be done by modifying the original image appropriately.
First Lego League
Panhellenic Educational Robotics Competition FLL 2023 | March 2023
Power Generation Pavement Slabs
Description:
Sidewalk tiles that generate energy using ceramic materials with piezoelectric properties.
Problem Addressed:
An inexpensive way to meet the electrical energy needs for urban lighting in spaces with high foot traffic, such as squares, shopping centers, sidewalks, etc.
Idea Description:
The idea is to manufacture sidewalk tiles with integrated ceramic plates with piezoelectric properties, allowing them to generate electricity when pedestrians walk over them. The tiles will be designed to create an electrical network upon installation, capable of powering the lighting needs of sidewalks, squares, commercial centers, and other facilities with a high human presence. The concept is to develop a standardized tile system that can be easily and affordably constructed, providing a sustainable lighting solution for public spaces and businesses.
Advantages:
Disadvantages:
Note that for actual implementation, the use of unified ceramic materials with piezoelectric properties is recommended. However, the prototype was built using sensors that are readily available in the market. Even with these sensors, each tile we created generated up to 20V! With the use of ceramic materials with more efficient piezoelectric properties and the utilization of a network of tiles traversed by a large number of pedestrians, both the voltage and (primarily) the current generated would be sufficient to illuminate a square.
Sidewalk tiles that generate energy using ceramic materials with piezoelectric properties.
Problem Addressed:
An inexpensive way to meet the electrical energy needs for urban lighting in spaces with high foot traffic, such as squares, shopping centers, sidewalks, etc.
Idea Description:
The idea is to manufacture sidewalk tiles with integrated ceramic plates with piezoelectric properties, allowing them to generate electricity when pedestrians walk over them. The tiles will be designed to create an electrical network upon installation, capable of powering the lighting needs of sidewalks, squares, commercial centers, and other facilities with a high human presence. The concept is to develop a standardized tile system that can be easily and affordably constructed, providing a sustainable lighting solution for public spaces and businesses.
Advantages:
- Standardizing the system into autonomous/separate tiles reduces development and installation costs.
- Simple to implement once standardized.
- Autonomous system operation without the need for external power supply.
- Highly efficient in areas with high pedestrian traffic (museums, squares, commercial walkways, etc.).
Disadvantages:
- Cost of materials and installation.
Note that for actual implementation, the use of unified ceramic materials with piezoelectric properties is recommended. However, the prototype was built using sensors that are readily available in the market. Even with these sensors, each tile we created generated up to 20V! With the use of ceramic materials with more efficient piezoelectric properties and the utilization of a network of tiles traversed by a large number of pedestrians, both the voltage and (primarily) the current generated would be sufficient to illuminate a square.
Mission Space Lab Phase 4
2023
Ιntroduction
The main objective of our experiment is to study the evolution of Harmful Algal Blooms, or HABs, over time because of ongoing climate change. HABs is defined as the rapid growth of flora in water resources that can cause harm to animals, humans, or the local ecosystem.
Nowadays, the amount of HABs has grown, causing several water contaminations. In our experiment, we will demonstrate that the rate of increase of HABs has accelerated in the last decade because of global warming, using recent data obtained from the ISS and past data from Google Earth Engine. Our research focused on Kainji Lake in North Central Nigeria and on the coast of Madagascar, for which we have managed to obtain a very good visualization from ISS.
Method
Our experiment divided into two parts: collecting and processing data.
For the first part, we developed a script in Python, with which we collected images of our planet, using the near-infrared camera. For each photo, in a csv file, we stored information, like latitude and longitude.
While we were waiting for the data, for the second part of the experiment, we developed a script that analyzes the image we want and calculates the NDVI of a specific area. Βased on the "Capture plant health with NDVI and Raspberry Pi" project, we adapted the code so that we could more quickly scan a folder of images and get the percentages of the vegetation index in specific areas of interest.
Once we received the data, we studied the photos, scanned them with the script we had built and decided to work on two specific photos that met the requirements of our experiment (cloud purity, water resources, HABs detection).
To compare historical data on the evolution of HABs in our areas of interest, we used Google Earth Engine, where we found older satellite images, measured the NDVI and compared it with our data.
Conclusion
Our efforts up to the 3rd phase of the competition were rewarded with the capture of perfect images of our planet. Although there were a lot of clouds, fortunately we were able to keep " clear" samples, which allowed us to do our analyses.
When studying the csv file data and the photos, we found a mismatch between the ISS geographic coordinates and the locations in the relevant images. Fortunately, however, this mismatch was fixed and corrected by adding a fixed factor (4 degrees) to the latitude and longitude of each image.
For the NDVI analysis of our own images, we relied on the technique available in the library of projects in the Raspberry Pi Foundation. For the search and analysis of satellite images from historical captures, we relied on the capabilities provided by Google Earth Engine, using the Python programming language. Since we used different techniques, there may be an inconsistency between these two measurements, but the result is confirmed, even visually for the case of Nigeria.
The main objective of our experiment is to study the evolution of Harmful Algal Blooms, or HABs, over time because of ongoing climate change. HABs is defined as the rapid growth of flora in water resources that can cause harm to animals, humans, or the local ecosystem.
Nowadays, the amount of HABs has grown, causing several water contaminations. In our experiment, we will demonstrate that the rate of increase of HABs has accelerated in the last decade because of global warming, using recent data obtained from the ISS and past data from Google Earth Engine. Our research focused on Kainji Lake in North Central Nigeria and on the coast of Madagascar, for which we have managed to obtain a very good visualization from ISS.
Method
Our experiment divided into two parts: collecting and processing data.
For the first part, we developed a script in Python, with which we collected images of our planet, using the near-infrared camera. For each photo, in a csv file, we stored information, like latitude and longitude.
While we were waiting for the data, for the second part of the experiment, we developed a script that analyzes the image we want and calculates the NDVI of a specific area. Βased on the "Capture plant health with NDVI and Raspberry Pi" project, we adapted the code so that we could more quickly scan a folder of images and get the percentages of the vegetation index in specific areas of interest.
Once we received the data, we studied the photos, scanned them with the script we had built and decided to work on two specific photos that met the requirements of our experiment (cloud purity, water resources, HABs detection).
To compare historical data on the evolution of HABs in our areas of interest, we used Google Earth Engine, where we found older satellite images, measured the NDVI and compared it with our data.
Conclusion
Our efforts up to the 3rd phase of the competition were rewarded with the capture of perfect images of our planet. Although there were a lot of clouds, fortunately we were able to keep " clear" samples, which allowed us to do our analyses.
When studying the csv file data and the photos, we found a mismatch between the ISS geographic coordinates and the locations in the relevant images. Fortunately, however, this mismatch was fixed and corrected by adding a fixed factor (4 degrees) to the latitude and longitude of each image.
For the NDVI analysis of our own images, we relied on the technique available in the library of projects in the Raspberry Pi Foundation. For the search and analysis of satellite images from historical captures, we relied on the capabilities provided by Google Earth Engine, using the Python programming language. Since we used different techniques, there may be an inconsistency between these two measurements, but the result is confirmed, even visually for the case of Nigeria.
First Lego League | Cargo Connect
Panhellenic Educational Robotics Competition FLL 2022 | March 2022
The Topic
During the 2021-2022 season, FIRST® teams explored the future of transportation. From delivering packages to rural and urban areas to providing assistance in disasters and high-tech aerial transportation, teams will imagine faster, more reliable, inclusive, and sustainable transportation innovations that connect and develop better communities and economies worldwide.
The Problem
One of the problems we face with cargo transportation is environmental pollution. Thousands of trucks and airplanes transport goods across the planet daily, significantly burdening the atmosphere. How often do you order something? Consider that each time you contribute to atmospheric pollution. Now, imagine this pollution millions of times greater, growing every day as the world orders food, toys, tools, televisions, computers, and much, much more.
The Idea
According to the current situation, airplanes are the fastest (and safest) means of transportation, but they are particularly expensive and polluting. What is the reason? Fuel, of course! An airplane needs enormous amounts of fuel to maintain its weight in the air and reach its destination. And this fuel, besides being expensive, emits huge amounts of carbon dioxide, severely harming the climate. A ground or sea transportation method can hardly compete with the safety and flexibility of air transportation. So, how could we design a hybrid commercial vehicle that can lift a container and transport it faster than trucks, while wasting less energy and following much shorter routes than those offered by the road network? Looking back at the way people traveled by air in the past, our idea was named "AirDrone," a combination of a Drone and an Airship.
During the 2021-2022 season, FIRST® teams explored the future of transportation. From delivering packages to rural and urban areas to providing assistance in disasters and high-tech aerial transportation, teams will imagine faster, more reliable, inclusive, and sustainable transportation innovations that connect and develop better communities and economies worldwide.
The Problem
One of the problems we face with cargo transportation is environmental pollution. Thousands of trucks and airplanes transport goods across the planet daily, significantly burdening the atmosphere. How often do you order something? Consider that each time you contribute to atmospheric pollution. Now, imagine this pollution millions of times greater, growing every day as the world orders food, toys, tools, televisions, computers, and much, much more.
The Idea
According to the current situation, airplanes are the fastest (and safest) means of transportation, but they are particularly expensive and polluting. What is the reason? Fuel, of course! An airplane needs enormous amounts of fuel to maintain its weight in the air and reach its destination. And this fuel, besides being expensive, emits huge amounts of carbon dioxide, severely harming the climate. A ground or sea transportation method can hardly compete with the safety and flexibility of air transportation. So, how could we design a hybrid commercial vehicle that can lift a container and transport it faster than trucks, while wasting less energy and following much shorter routes than those offered by the road network? Looking back at the way people traveled by air in the past, our idea was named "AirDrone," a combination of a Drone and an Airship.
First Lego League | Cargo Connect
Panhellenic Educational Robotics Competition FLL 2022 | March 2022
Cargo theft is a global threat that triggers chain reactions in the supply chain: loss of cargo, delays in deliveries, threats to the physical integrity of transportation workers, price increases due to cargo losses, and much more. According to the latest report from the TT Club (Through Transport Club), one of the world's largest transport and logistics security companies, 71% of cargo thefts occur on trucks, compared to thefts in warehouses or facilities.
Our proposal is the development of a comprehensive monitoring and control system for cargo-carrying trucks. It consists of a set of simple and more complex mechanisms that, when applied collectively, will prevent many theft attempts on trucks, covering all the aforementioned categories. The system can be developed in three (3) major parts:
We sent our idea to many transportation companies in Greece as well as to truck drivers and received very positive feedback. In fact, one of these companies suggested adding a panic button in the truck's cabin. Through the Vehicle Control Unit, the driver can inform the Control Center about any danger. We found this to be a very good idea and implemented it.
Our proposal is the development of a comprehensive monitoring and control system for cargo-carrying trucks. It consists of a set of simple and more complex mechanisms that, when applied collectively, will prevent many theft attempts on trucks, covering all the aforementioned categories. The system can be developed in three (3) major parts:
- Control Unit of the System
- System Control Center
- System Communication Protocol
We sent our idea to many transportation companies in Greece as well as to truck drivers and received very positive feedback. In fact, one of these companies suggested adding a panic button in the truck's cabin. Through the Vehicle Control Unit, the driver can inform the Control Center about any danger. We found this to be a very good idea and implemented it.
Mission Space Lab
2022
Introduction
Climate change is an important problem which is currently concerns everyone. That is why our team aimed to investigate how diverse types of clouds affect global warming. During our research, we found out that there are two basic types of clouds: the ones that act like parasols and the ones that act like blankets. Clouds within a mile or so of Earth’s surface tend to cool more than they warm (parasols). These low, thicker clouds mostly reflect the Sun’s heat. This cools Earth’s surface. Clouds high up in the atmosphere have the opposite effect: They tend to warm Earth more than they cool (blankets). High, thin clouds trap some of the Sun’s heat. This warms Earth’s surface. The aim of this scientific experiment was to see if we can measure how many clouds benefit or do not benefit the earth. We tried to:
• Classify the blanket and parasol clouds
• Find the percentage of how much each one covers the earth (based on the orbit of the ISS)
• Compare our results with past results and
• Study the distribution of the different cloud categories and how it affects the evolution of the phenomenon of climate change.
Method
Here is the technical part. But primarily, we must talk about the Hardware. Obviously, we used a Raspberry Pi. The only sensors we used were the camera.
The type of data we wanted was:
• Images
• Coordinates of where the picture was taken
We have two phases to the program, the space phase, and the Earth phase. The space phase where we capture the photos from the ISS and the second phase which starts from the point that we receive the photos from the ISS and start analysing them using the Coral and Teachable Machine We used two methods to analyse the data:
• Machine learning (Teachable Machine, Coral)
• OpenCV We started with Teachable Machine and Coral for which we imported the data to them to give us an ai model back that could give us the result.
Moving on to the OpenCV part we developed a script to determine how much of the earth's surface was covered by clouds. Also, we used the K-Means algorithm, k-Means uses the distance between points as a measure of similarity, based on k averages (i.e. means). All our work is freely accessible on GitHub.
Learnings
The idea to participate in the Space Mission Lab impressed us, but at first it scared us a little. What experiment could we do with data from space, without ever dealing with this? Initially we studied data from the competition, from previous years. We noticed that in most of the photos on our planet there were clouds. Of course, the internet helped a lot. The search began, with the key "clouds" and the idea quickly came: climate change and how it is affected by different types of clouds… Once we understood the blanket/parasol’ s phenomenon, we designed the different phases of the experiment and shared them to the team. The process of deconstruction of our experiment seemed quite difficult, but very effective, to be able to "break" it in different phases. We also had our first repository on GitHub, the idea and its capabilities impressed us. We know that the use of more advanced methods, as we write in the conclusion, would give us more reliable results. We look forward to the next time we are given the opportunity to learn new techniques and apply them to deal with a real problem!
Conclusion In both different image samples, the results are not far apart. Although the percentage of coverage was small, we believe that even at a larger scale analysis, the “parasol” clouds will be more than the “blankets”. There are certainly many assumptions in our experiment, such as we ignored images with few clouds, we did not measure different types of clouds in the same image, loss of information due to common confident K in the K–means algorithm and impossibility to separate clouds from white surfaces (e.g. snowy areas). One way to deal those problems might be to use more advanced AI techniques, such as Sematic Segmentation or Mono Depth, to identify and measure different types of clouds more accurately in the same image. This is our next target! But why is the systematic study of this phenomenon important? Knowing that some types of clouds are slowing down the evolution of climate change and others are accelerating it, we believe that an extensive study of the phenomenon will help us to predict climate change-related indicators. Clouds are an important parameter that we should not ignore and why not, in the future we could use it for the benefit of our planet!
Climate change is an important problem which is currently concerns everyone. That is why our team aimed to investigate how diverse types of clouds affect global warming. During our research, we found out that there are two basic types of clouds: the ones that act like parasols and the ones that act like blankets. Clouds within a mile or so of Earth’s surface tend to cool more than they warm (parasols). These low, thicker clouds mostly reflect the Sun’s heat. This cools Earth’s surface. Clouds high up in the atmosphere have the opposite effect: They tend to warm Earth more than they cool (blankets). High, thin clouds trap some of the Sun’s heat. This warms Earth’s surface. The aim of this scientific experiment was to see if we can measure how many clouds benefit or do not benefit the earth. We tried to:
• Classify the blanket and parasol clouds
• Find the percentage of how much each one covers the earth (based on the orbit of the ISS)
• Compare our results with past results and
• Study the distribution of the different cloud categories and how it affects the evolution of the phenomenon of climate change.
Method
Here is the technical part. But primarily, we must talk about the Hardware. Obviously, we used a Raspberry Pi. The only sensors we used were the camera.
The type of data we wanted was:
• Images
• Coordinates of where the picture was taken
We have two phases to the program, the space phase, and the Earth phase. The space phase where we capture the photos from the ISS and the second phase which starts from the point that we receive the photos from the ISS and start analysing them using the Coral and Teachable Machine We used two methods to analyse the data:
• Machine learning (Teachable Machine, Coral)
• OpenCV We started with Teachable Machine and Coral for which we imported the data to them to give us an ai model back that could give us the result.
Moving on to the OpenCV part we developed a script to determine how much of the earth's surface was covered by clouds. Also, we used the K-Means algorithm, k-Means uses the distance between points as a measure of similarity, based on k averages (i.e. means). All our work is freely accessible on GitHub.
Learnings
The idea to participate in the Space Mission Lab impressed us, but at first it scared us a little. What experiment could we do with data from space, without ever dealing with this? Initially we studied data from the competition, from previous years. We noticed that in most of the photos on our planet there were clouds. Of course, the internet helped a lot. The search began, with the key "clouds" and the idea quickly came: climate change and how it is affected by different types of clouds… Once we understood the blanket/parasol’ s phenomenon, we designed the different phases of the experiment and shared them to the team. The process of deconstruction of our experiment seemed quite difficult, but very effective, to be able to "break" it in different phases. We also had our first repository on GitHub, the idea and its capabilities impressed us. We know that the use of more advanced methods, as we write in the conclusion, would give us more reliable results. We look forward to the next time we are given the opportunity to learn new techniques and apply them to deal with a real problem!
Conclusion In both different image samples, the results are not far apart. Although the percentage of coverage was small, we believe that even at a larger scale analysis, the “parasol” clouds will be more than the “blankets”. There are certainly many assumptions in our experiment, such as we ignored images with few clouds, we did not measure different types of clouds in the same image, loss of information due to common confident K in the K–means algorithm and impossibility to separate clouds from white surfaces (e.g. snowy areas). One way to deal those problems might be to use more advanced AI techniques, such as Sematic Segmentation or Mono Depth, to identify and measure different types of clouds more accurately in the same image. This is our next target! But why is the systematic study of this phenomenon important? Knowing that some types of clouds are slowing down the evolution of climate change and others are accelerating it, we believe that an extensive study of the phenomenon will help us to predict climate change-related indicators. Clouds are an important parameter that we should not ignore and why not, in the future we could use it for the benefit of our planet!
1st Award of Innovative Solution
Panhellenic Educational Robotics Competition FLL 2019 | March 2019
Soft Robots in Space
Soft Robotics is a relatively new branch of robotics that has developed in recent years. It is a specialized area of robotics that deals with non-rigid robots constructed from soft and deformable materials such as silicone, plastic, and rubber.
Inspired by nature, soft robotics utilizes structures and mechanisms that closely mimic how living organisms move and adapt to their environment. The ability to deform is crucial for robots facing uncertainty in extremely harsh environments, such as collecting unknown-sized objects and moving on rough and uncertain terrain.
Furthermore, unlike robots made of rigid materials, soft robots allow greater flexibility and adaptability for performing tasks, as well as improved safety when collaborating with humans.
The primary goal of the system is to demonstrate some of the advantages that Soft Robotics technology brings to actuators. This goal should not be perceived as an attempt to prove that soft actuators are "better" than traditional (hard) actuators, as Soft Robotics technology cannot replace current technology but can assist in addressing specific problems. The system is controlled by a central console and can be remotely controlled (Bluetooth) through an Android application developed for this purpose.
ADVANTAGES:
Inspired by nature, soft robotics utilizes structures and mechanisms that closely mimic how living organisms move and adapt to their environment. The ability to deform is crucial for robots facing uncertainty in extremely harsh environments, such as collecting unknown-sized objects and moving on rough and uncertain terrain.
Furthermore, unlike robots made of rigid materials, soft robots allow greater flexibility and adaptability for performing tasks, as well as improved safety when collaborating with humans.
The primary goal of the system is to demonstrate some of the advantages that Soft Robotics technology brings to actuators. This goal should not be perceived as an attempt to prove that soft actuators are "better" than traditional (hard) actuators, as Soft Robotics technology cannot replace current technology but can assist in addressing specific problems. The system is controlled by a central console and can be remotely controlled (Bluetooth) through an Android application developed for this purpose.
ADVANTAGES:
- Sending robots to explore space without worrying excessively about their safety. With careful design and construction, they can remain functional at their destination for a long time, aiming to send data for processing.
- Cheaper to send a robot into space than a human. This is because robots can remain functional in space for many years, and there is no need to organize a return trip. According to Space Law, space exploration must comply with the Rules for the Protection of the Space Environment.
- Robots can withstand harsh conditions such as extreme temperatures and high levels of radiation, including exposure to cosmic radiation.
Internet of Things & Architecture
September 2018 | Presentation at the Researcher's Night
Our participation in the Researcher's Night at NCSR "Demokritos". The project was based on an architectural design from which we borrowed the plans and created a model to turn it into a smart home! Internet of Things & Architecture. High school students developed a program to control lighting in each room of the house, open and close the external shades, and raise and lower the partition between the kitchen and the living room/work area.
The project was based on designs by Interior Architects Moira Douranou, Stella Jartou, and Peggy Kombotea.
In a time when we live with fewer resources, we learn to appreciate the elements of nature more. The sun, abundant in our country, and the diffusion of its energy and light inspired us to create a unified space easily separable for the multiple needs of the users. The central element is the volume of the bathroom that separates and unifies the space. A system for displaying the designer's works or entertainment movies slides and separates the functions. A second main entrance provides independent access to the office area. Easy transformation or storage of furniture for different uses of the spaces has been provided.
The project was based on designs by Interior Architects Moira Douranou, Stella Jartou, and Peggy Kombotea.
In a time when we live with fewer resources, we learn to appreciate the elements of nature more. The sun, abundant in our country, and the diffusion of its energy and light inspired us to create a unified space easily separable for the multiple needs of the users. The central element is the volume of the bathroom that separates and unifies the space. A system for displaying the designer's works or entertainment movies slides and separates the functions. A second main entrance provides independent access to the office area. Easy transformation or storage of furniture for different uses of the spaces has been provided.