ClustÆr

As a complex problem, induced climate change is probably the main challenge of the XXI century and it has no unique solutions. Many efforts to tackle pollution should be conceived in relation to others. ClustÆr is a project that articulates a plot between a Swarm Intelligence (S.I) composed by self-organized quadcopters, sensor networks that detect the most polluted regions of a city (Mexico City in this case) and techniques of artificial photosynthesis to transform CO2 in energy and oxygen through tiny photocatalytic reactors. The main goals are to develop an efficient new technological strategy at a small scale as the first stage, and to contribute with significant research in nano-swarm systems that are able to chemically neutralize four of the main toxic gases harming the atmosphere. 

Current Stage: Prototype of Swarm Simulations. We are looking now for funds to make an implementation on a Swarm of quadcopters.

In May of 2019 the concentration levels of CO2 in the atmosphere reached the highest peak ever according to the measurements taken by meteorologists from the University of San Diego through the Mauna Loa Observatory. This fact is a consequence of decades of unprecedented emissions. The carbon-based economy is showing clear signs not only of obsolescence but of urgency to be shifted. Society has built powerful incentives around this economic engine. It seems the change to clean energy alternatives will be so slow that that the integrity of the globe and many species is commited. 

The UN Intergovernmental Panel on Climate Change (IPCC) estimates that the limit to implement decisive politics and actions is 2030 before the effects of global warming are irreversible and the ecosystems enter in decay. Beyond having a solutionist approach to this problem, we should think about it as an adaptive and changing problem so our responses should have the same nature. 

Global Warming is affecting everyone indiscriminately, even its negationists. But as in every circumstance of vulnerability there are sectors of the population that are more exposed to immediate griefs as the lack of access to basic services like drinking water or being casualties of fires, storms, and other environmental phenomena. 

In the cities with the highest concentration of toxic gases, the poor and middle class areas tend to be the most harmed ones. ClustÆr proposal is based on the belief that problems should be tackled where they are most urgent, to significantly contribute to reducing global warming & its negative social effects too.

ClustÆr is a Swarm of nano drones which will play different dynamics of self-organization inspired by flocks in nature. Each unit will contain a photocatalytic nanosheet or micro reactor module that is able to absorb four of the main toxic gases in the atmosphere (NO, CO2, NO2 and CO) and create oxigen transforming the neutralized pollutants into energy to power the system. Making it circular and self-sustainable. Every quadcopter  will be designed using bio-3D printing techniques and will no implicate toxic components in its fabrication. Each unit will be self-sustainable and low energy consumption.

Our team will perform experiments and computer simulations of swarm dynamics to apply the most efficient and to reach a point where the systems can coordinate itself according with the gas sensor indicators of CO2, NO, NO2 and CO and locate them via GPS to do air cleaning routines. With the help of the sensors (Nanoz) and the monitoring systems of the photocatalytic module we expect to be able to monitorize the data of how much pollution the system has erradicated in terms of particles standard measurements as Parts Per Million (ppm).

This project does not pretend to solve by itself the whole problem but it´s an alternative about how to use existing techniques in a different way and hopefully it can be an inspiring initiative to thrigger more projects that confront the problem from different perspectives and scales.  

The intersection of existing technologies that hasn´t previoulsy converged in the same way. Drones for instance are widely used as cameras, as scanning or monitoring devices. ClustÆr aims to blend this functionality and to perform functions of a plant where the polluting gases concentrates: the Troposphere which comprehends from the ground to 5 miles of altitude, this could have meaningful advantages compared with the photosynthesis that occurs at the ground level. 

Another innovative aspect is the local coordination of the swarm. I´ve been doing computer simulations of different social organisms to understand which could be the best strategy based in self organisation dynamics and if that would be more efficient than explicit remote control.

Moreover the adaptive behaviour and scalability of the swarm to different conditions 

°Swarm Intelligence (S.I.) Swarm algorithms in Python, Logo and C# to set  different flocking dynamics of coordination and find the most optimal to achieve self-organisation of the quadcopters as a Scatternet

°Photocatalyst foam TiO2 micro-reactors as surfaces which function is the absortion of CO2, NO2 and other gases and transform then in Oxygen, the reaction produces energy that will be used to mantain the low power consumption of the signal emission of every drone for internal coordination. The photocatalyst nanosheet consists in an array-like bismuth oxyhalides (BiOX) and polyurethane foams (PUF).

°Drones

°Green 3D-printing to substitute the plastic components of the quadcopters

°Custom microprocessors & sensor networks that make measurements of gas emissions and let the system to make smarter. decisions

°Hadoop, Spark Apache for managing the data produced by the sensors in the stations and the sensors of the units.

°Machine Learning strategies to make statistical inference of the data and improve the decisions of the system.

The problem of the air pollution that cause global warming is huge and a  planetary scale problem. The relevance of this proposal is to contribute to join to the diversity of alternatives and nourish a creative approach. The problem can be handled from different perspectives simultaneously as creating politics and make pressure on carbon-based corporations, or taking everyday actions, etcetera doesn´t matter how small they are. The approach is more contributive than fully solve the problem.

ClustÆr expects to face a preexisting pollution condition at a local scale through different strategies as making the swarm semi-autonomous or guiding it to a particular point and develop a swept routine. 

Taking advantage of the efficiency  of making the gas absorbtion tens of meters above the ground we expect to test different measurements to collect data that will let us  quantify how much CO2 was absorbed and how much Oxygen was liberated in terms of parts per million of particle concentration and comparing it with a more familiar equivalence as for example "during the day routine the system reduced the CO2 equivalent to 100 vehicles".

Our solution will be able to unfold in different stages and scales. Considering the swept in city quadrants the benefited people would reach tens of thousands.

Next year 

Having tested our prototype in a densely polluted real scenario and quantified the efficiency of the swarm, we will immediately release the project for venture & seed capitals to invest in a larger scale version of the project.

Next Five years 

In this lapse we expect to have developed at least one version for each branch of our deployment modalities: NGOs investment, Government contractor and market demand. With this we will have enough data and fast know how to grow exponentialy and have a real estimation of the social and environmental impact.

°The massive scale of the problem.

°The low financial support  & development  in the LATAM region and particulary in Mexico is a key barrier but at the same time it´s an opportunity of growth.

°The limitations of the adoption of technologies in specific in the public sector where legislations reduce the innovation incentives.

°The low-height permission of non-tripulated flights for drones in different cities that have different legislations.

°By scaling and diversifying the possibilities of buiding an ecosystem of swarms, increasing its capabilities of geographic coverage.

°By expanding the interlocution beyond the regional context and linking with foreign agents, organizations, institutions, universities with afinities to achive the financial and other resources.

°By implementing an educational component in the diffusion strategy of the project for non-specialists in the public and private sector aswell the general population.

°By setting agreements with local authorities arguing the evident priority of attending the environmental harm.

Project lead: 1

Scientific board advisors: 5

Hardware designer: 2

Programming team: 3

After ten years of developing different skills and specialized knowledge in creative technologies I continue reinforcing my background.As a cultural producer I have managed more than 20 exhibitions and projects related with virtual reality, AR, 3D simulation, among other digital media. The key component of this projects is social engagement. Actually I am managing my biggest project to date in Mexico City that combines tech research and critical implementations of technology in the political arena. Simultaneously I keep improving my knowledge studying complexity science, adaptive systems and programming. I´m going deeper in my studies about theoretical computer science, sciences of complexity and Machine Learning paradigms. My goal as a professional is to merge my creative background with computer science to create useful tools and critical approaches for social good.

The team by now is small but diverse in background and skills:

The three scientists are researchers from the best two universities in Mexico UNAM and IPN. Their doctoral researches have to do with photosynthesis, chemical reactions, science of materials and they recently patented a new method to fabricate photocatalityc concrete. Also we have a posdoc whose domain is self-organisation, complex systems and manages the National Laboratory of Complexity in Mexico City.

Our hardware and programming team are graduates from the IPN and they specialize in custom circuit design, IoT, communication protocols, among others.

We are in the initial stage. That´s why is crucial to have the opportunity of engaging with different agents as organizations, enterprises, startups, scientists, technologiests and people in general.

ClustAer is conceived ad a non-profit project targeting regions of Mexico City that are not benefited by any socio-ecological program, but the funding mechanisms to deploy the system and make sustainable the accelerated research and development will be: 

-Making a public contractor-offer to the environmental dependencies of the government

- Agreements with the private sector to outline a council and a patronage

- Make a convergence of environmental foundations to create a donation fund 

-To pursuit seed capital / venture capital to develop a customized version for a specific city or region. 

Our team is based in Mexico City. The conditions to develop a project with this characteristics have many challenges: technological, contextual, lack of local platforms that can help to generate the environment required, among others.

We are enthusiastic about MIT Solve because the platform reaches the capacity to help us building this initiative and particulary, give us a meaninful feedback during the process until the project is consolidated.

The United Nations Climate Summit 

Any foundation and NGO interested in Climate Change, Environmental causes and Technology

Open AI

Tamás Vicsekand his research group in Vrije Universiteit, Amsterdam

We are developing Swarm Intelligence simulations which could be considered a part in the vastness of AI taxonomy. ClustAer expects to  implement & improve intelligent flocking dynamics to our quadcopters in its aim to clean polluted air and evolve the intelligence of the system finding the most optimal and efficient realization of its tasks.

Our project implies learning from the data generated by a sensor network of a swarm of quadcopters and the air quality monitoring stations. Through different tools we aim to interpret and take advantage of machine learning and statistical inference to make our project be more efficient.