Grasso, Paolo

Innocente, Mauro Sebastián

Swarm robotics and Unmanned Aerial Vehicles (UAVs) technology have been progressing at an increasingly fast pace for the past two decades, thus extending their capabilities and the kinds of problems they can help tackle. UAVs can now be equipped with a range of advanced devices and sensors which enable them to navigate remote areas and to operate in dangerous environments. Given the hazardous nature of the activity, fighting fires by means of disposable and relatively inexpensive robots in place of humans is of special interest. In addition, the use of fleets of decentralised cooperative and self-organising robots results in a robust and resilient system with distributed decision-making which can cope with uncertainty, errors, and the failure or loss of a few nonessential units without jeopardising the overall mission. This paper comprises an initial proof-of-concept to demonstrate the feasibility and tremendous potential of employing swarm robotics to fight fires autonomously. Thus, an efficient yet realistic model of the spread of fire is developed, which is then coupled with a model of a fleet of self-organising drones whose coordination mechanism is based on a forgetful particle swarm algorithm

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102 UK-RAS CONFERENCE: 'ROBOTS WORKING FOR & AMONG US' PROCEEDINGS (2017) Proof-of-Concept Swarm of Self-Organising Drones Aimed at Fighting Wildfires Mauro S. Innocente and Paolo Grasso, Smart Vehicles Control Laboratory, Institute for Future Transport and Cities, Coventry University I. INTRODUCTIONSwarm Intelligence (SI) comprises arelatively novel route to ArtificialIntelligence (AI) which stems fromdecentralised and self-organising behaviourobserved in groups of simple social animalsin nature such as ant and bee colonies, fishschools, and bird flocks. By way ofcollaboration, these animals are able toaccomplish tasks that are far beyond theirindividual capabilities, and even beyond thesimple aggregation of all of their individualcapabilities. That is to say, the whole is morethan the sum of its parts. Thus, SI is thebranch of AI that deals with the collectivebehaviour that emerges from decentralisedself-organising systems. Self-Organisationoccurs with no central control or sense ofpurpose, as individuals only interact locallywith one another and with the environment,inducing the emergence of coherent globalpatterns. Swarm robotics is a novel approachto the coordination of large numbers ofsimple and relatively inexpensive robots,which emerged as the application of SI tomulti-robot systems. Different from other SIstudies, swarm robotics puts emphasis on thephysical embodiment of individuals [1].Swarm robotics and Unmanned Aerial Vehicles (UAVs) technology have progressed at an increasingly fast pace for the past two decades, extending their capabilities and the kinds of problems they can help tackle. UAVs can now be equipped with a range of advanced cameras, thermal imagers and sensors which enable them to operate in remote areas, dangerous environments, and even through solid smoke while still being able to perform tasks such as surveying, mapping or locating people. Some of their current applications include aerial photography and filming, information gathering, provision of essential supplies for disaster management, support of search and rescue operations, mapping inaccessible locations, field surveying, and crop health monitoring. Autonomous UAVs have been used to establish resilient communications networks for emergency response [2] [3]. With regards to fire-fighting, UAV technology has been used to perform tasks such as forest surveillance, building fire risk maps, wildfire detection and monitoring, gathering data for a human decision-maker, assisting search and rescue operations, and situational awareness [4]. Given the hazardous nature of the activity, fighting fires by means of disposable and relatively inexpensive robots in place of humans is of special interest. In addition, the use of fleets of decentralised cooperative and self-organising robots results in a robust and resilient system with distributed decision-making which can cope with uncertainty, errors, and the failure or loss of a few nonessential units without jeopardising the overall mission. However, to the best of our knowledge, the use of self-organising swarms of autonomous UAVs for the actual deed of putting out fires has remained notably unexplored. This paper comprises a proof-of-concept to demonstrate the feasibility and potential of employing swarm robotics to fight fires autonomously. To this end, an efficient yet realistic physics-based model of the spread of wildfires is developed, which is then coupled with a model of a fleet of self-organising drones whose coordination mechanism is based on a forgetful particle swarm algorithm. The aim is to develop algorithms for swarms of autonomous drones to self-organise to develop the ability to fight the spread of wildfires without human intervention. This research is timely, since Robotics and AI (RAI) as well as Robotics UK-RAS CONFERENCE: 'ROBOTS WORKING FOR & AMONG US' PROCEEDINGS (2017) 103 and Autonomous Systems (RAS) are identified as priority areas by the Engineering and Physical Sciences Research Council (EPSRC), as well as in the Industrial and Digital Strategies [5], [6] [7]. In addition, the UK government identified RAS as one of the Eight Great Technologies that will propel the UK to future growth [8], [9]. This topic is also supported by the UK-RAS Network which predicts that RAS will play an increasingly important role in disaster relief by reducing costs and response times and increasing capabilities [10]. II. FIRE-SPREAD MODELA physics-based fire-spread model wasdesigned and implemented as a two-dimensional reaction-diffusion equation thatdescribes the combustion of a mono-phasemedium composed of pre-mixed gas of fueland air.The system is closed with the equations for the molar mass of the mixture, the heat capacity of the mixture, the combustion rate (Arrhenius equation), and the combustion enthalpy. Fig. 1 shows an example of this model being used for the prediction of the temperature profile and fuel energy density five minutes after four sparks have occurred. While very efficient, the simulation of this model using Matlab and a standard PC still  This comprises a simplified version of the model in [11]. It is also assumed that there is no atmospheric wind and the transport equations can therefore be neglected.  In order to compensate for this, the diffusion coefficient is increased and two pseudo-3D terms are added into the energy balance equation to account for the energy losses due to convection and radiation in the third dimension.  Radiation in the horizontal directions is modelled to affect only the neighbour cells. The heat capacity at constant pressure is assumed to be constant for each chemical species within the considered temperature range. The fire-spread model can be represented by a system of five partial differential equations, one for the enthalpy balance and four for the chemical species formation (CO2 and H2O) or consumption (Fuel and O2):  runs about five times slower than real-time (without visualisation). III. SELF-ORGANISATION MODELAt this early stage of the research, the swarmof drones has been modelled as 2D masslessparticles whose self-organisation is based onthe particle swarm algorithm. Modificationswere introduced to handle particles’memories in a dynamic environment, and toFig. 1: Simulation of fire-spread model. The first figure shows the temperature profile once 4 sparks have occurred, the second one shows the temperature profile after 5 minutes, and the last one shows fuel energy density after 5 minutes (black: no fuel). UK-RAS CONFERENCE: 'ROBOTS WORKING FOR & AMONG US' PROCEEDINGS (2017) 104 control the level of stochasticity so as to smoothen the erratic behavior. While the fire model is updated every quarter of a second, sensor measurements and drones’ memories are updated every second. Maximum speed permitted is set to 10 m/s. I.V. FUTURE WORKFuture work includes the incorporation ofcollision avoidance algorithms, and themodelling of the actual drones to includeflight dynamics and local controllers.We have also developed a more advancedfire- spread model which accounts for transport phenomena due to varying pressure and temperature inspired by [12] and [13], though atmospheric wind is yet to be included.  However, this model is too demanding to be used extensively. We are also exploring the use of the Fire Dynamics Simulator (FDS) [14], the FIRESITE wildfire growth simulator [15], and of models based on cellular automata and Lattice Boltzmann. REFERENCES 1. Şahin, S. Girgin, L. Bayindir and A. E.Turgut, “Swarm Robotics,” in SwarmIntelligence. Natural2. Computing Series, C. Blum and D. Merkle,Eds., Springer, Berlin, Heidelberg, 2008, pp.87-100.3. S. Hauert, S. Leven, J.-C. Zufferey and D.Floreano, “The swarming micro air vehiclenetwork (SMAVNET) project,” [Online].Available:http://lis2.epfl.ch/CompletedResearchProjects/SwarmingMAVs/index.php. [Accessed 1201 2018].4. J. Ueyama, H. Freitas, B. S. Faiçal, G. P.Filho, P. Fini, G. Pessin, P. H. Gomes and L.A. Villas, “Exploiting the use of unmannedaerial vehicles to provide resilience inwireless sensor networks,” IEEECommunications Magazine, pp. 81-87, 2014.5. J. C. Jones, “SMART fire fighting: the use ofunmanned aircraft systems in the fireservice,” NFPA 2015 Responder Forum,2015.6. “Building our Industrial Strategy,” HMgovernment green paper, 2017. 7. “Industrial strategy. Building a Britain fit forthe future,” HM government white paper,2017.8. “The Digital Strategy,” HM governmentpolicy paper, 20179. Willets, “Eight Great Technologies,” PolicyExchange, London, 2013.10. “Robotics and Artificial Intelligence, FifthReport of Session 2016-17,” House ofCommons Science and TechnologyCommittee, 2016.11. “Extreme Environments Robotics: Roboticsfor Emergency Response, Disaster Relief andResilience,” UK-RAS white papers, 2017.12. L. Ferragut, M. I. Asensio and S. Monedero,“A numerical method for solvingconvection–reaction– diffusion multivaluedequations in fire spread modelling,”Advances in Engineering Software, vol. 38,pp. 366-371, 2007.13. Séro-Guillaume and J. Margerit, “Modellingforest fires. Part I: a complete set ofequations derived by extended irreversibleFig.	2:	Simulation	of	self-organising	drones	fighting	the	spread	of	a	wildfire.	The	first	figure	shows	temperature	100	seconds	after	4	sparks	have	occurred,	the	second	one	shows	the	same	after	5	minutes,	and	the	last	one	shows	fuel	energy	density	after	5	minutes.	 UK-RAS CONFERENCE: 'ROBOTS WORKING FOR & AMONG US' PROCEEDINGS (2017) 105 thermodynamics,” International Journal of Heat and Mass Transfer, vol. 45, pp. 1705-1722, 2002. 14. J. Margerit and O. Séro-Guillaume,“Modelling forest fires. Part II: reduction totwo-dimensional models and simulation ofpropagation,” International Journal of Heatand Mass Transfer, vol. 45, p. 1723–1737,2002.15. K. McGrattan, S. Hostikka, R. McDermott, J.Floyd, C. Weinschenk and K. Overholt, “FireDynamics Simulator Technical ReferenceGuide. Volume 1: Mathematical Model,”VTT Technical Research Centre of Finland,2013.16. A. Finney, “FARSITE: Fire area simulator- model development and evaluation,” U.S.Department of Agriculture, Forest Service,Rocky Mountain Research Station, 2004.

Proof-of-Concept Swarm of Self-Organising Drones Aimed at Fighting Wildfires

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Proof-of-Concept Swarm of Self-Organising Drones Aimed at Fighting Wildfires

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