This paper describes a group of robots for cleaning a simulated environment and proposes an ecient algorithm for navigation based on Path nding A *. No need for vision sensors. As a result it was observed that the robots can work cooperatively to clear the ground and that the navigation algorithm is e ective in cleaning. In order to test its eciency it was compared the combination of the Path nding A* algorithm and the decision algorithm proposed in this paper with Path nding A* and Euclidean distance, resulted in an improvement in time and distance traveled

Costa, NJC

Fonseca Ferreira, N. M.

M Sousa, Jose Vigno

Pontes De Araujo, Thelmo

Saraiva, Arata A.

Valente, Antonio

Repositorio Institucional de la Universidad Tecnológica de Panamá

Scalable Task Cleanup Assignment for Multi-agents

Portal de Revistas Académicas UTP (Universidad Tecnológica de Panamá)

August 21, 2018 9:17 WSPC - Proceedings Trim Size: 9in x 6in ws-procs9x6Scalable Task Cleanup Assignment for Multi-agentsArata A. Saraiva1UTAD University,Coimbra, PortugalORCID 0000-0002-3960-697X 1E-mail: aratasaraiva@gmail.comCosta. NJC2State University of Piaui,Piripiri, Piaui, BazilORCID 0000-0001-5636-424X 2E-mail: trisna@prp.uespi.brJose Vigno M Sousa3State University of Piaui, Piripiri,BrazilUniversity Brazil, Sao Paulo, BrazilORCID: 0000-0002-5164-360X 3E-mail: vigno@prp.uespi.brThelmo Pontes De Arau´jo4Ceara state University, Fortaleza, Ceara, Brazil4E-mail: thelmo.dearaujo@gmail.comN. M. Fonseca Ferreira5INESC-TEC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, 378,4200 - 465 Porto, Portugal,Knowledge Engineering and Decision-Support Research Center (GECAD) of theInstitute of Engineering, Polytechnic Institute of PortoDepartment of Electrical Engineering, Institute of Engineering of Coimbra,Coimbra, Polytechnic InstituteRua Pedro Nunes, 3031-601 Coimbra, PortugalORCID: 0000-0002-2204-6339 5E-mail: nunomig@isec.ptAntonio Valente6INESC-TEC Technology and ScienceCampus da FEUP, Rua Dr. Roberto Frias, 378, 4200 - 465 Porto, PortugalSchool of Science and Technology, University of Tra´s-os-Montes and Alto Douro,Vila Real, PortugalORCID: 0000-0002-5798-1298 6E-mail: avalente@utad.ptH. Montes et al. (eds.), CLAWAR 2018: 21st International Conference on Climbing andWalking Robots and the Support Technologies for Mobile Machines, Robotics Transforming the Future© ELSEVIERAugust 21, 2018 9:17 WSPC - Proceedings Trim Size: 9in x 6in ws-procs9x6440This paper describes a group of robots for cleaning a simulated environmentand proposes an efficient algorithm for navigation based on Pathfinding A *.No need for vision sensors. As a result it was observed that the robots can workcooperatively to clear the ground and that the navigation algorithm is effectivein cleaning. In order to test its efficiency it was compared the combination ofthe Pathfinding A* algorithm and the decision algorithm proposed in this paperwith Pathfinding A* and Euclidean distance, resulted in an improvement intime and distance traveled.Keywords: Multi robots; Cleaning; Cooperative work; Navigation.1. IntroductionThe issue of protecting the environment from pollution is necessary andespecially by the fact that natural resources are under threat. The humanimpact on the environment is undeniable and can be altered by the use ofautonomous robotics.Utility companies can benefit by reducing routine work, reaching hard-to-reach regions, and can perform service at scheduled times.1 During har-vesting, the robot moves independently on the given surface, removinggarbage from it. Having found an obstacle in the way, the robot decidesa method to overcome it based on special algorithms.2The type of autonomous system to be developed takes into account thepossibility of combining several simple yet functional techniques that arealready known in the literature to solve the problem of having mobile agentsidentify and follow to soar, dirty target.In the project it is proposed to use Breadth First Search (BFS) to dothe mapping using the target pattern. This target will be arranged in aplane position and orientation environment, so the Euclidean distance isused to plot the route. Based on these readings the positioning controlwould be applied so that it can follow the target within the environment.A comparison was made by merging the navigation algorithm PathfindingA *, Euclidean distance and the algorithm proposed in this work withPathfinding A *, Euclidean distance.The document is divided into 5 sections, in which section 2 is charac-terized by the contextualization of the work. It follows the methodologyapplied in section 3. The results after the application of the proposal arepresented in section 4 and the conclusion in section 5.2. Contextualization of workSurface cleaning robots are environmental protection equipment used pri-marily to collect garbage. According to the task requirements, they can beAugust 21, 2018 9:17 WSPC - Proceedings Trim Size: 9in x 6in ws-procs9x6441used to monitor the environment of parks, orchards, squares and accumu-lating information through sensors and performing dangerous operationssuch as search and rescue. They are also important tools in assessing en-vironmental pollution, providing warnings and disaster prevention, whichprovides enormous economic and social benefits in addition to possible po-tential applications34.5One of the potential applications is in the work of6 that a vacuumcleaning system is proposed integrated with a fuzzy inference system toimprove the accuracy of the system. In7 a system of automatic control of aroom cleaning robot with cellular control capability using DTMF with theability to avoid self-controlled obstacles, together with a system of cleaningof residues and dust is developed.In8 proposes an improvement in cleaning efficiency not because of ob-stacles and route planning. To solve this problem it develops a localizationalgorithm with the use of stereoscopic vision and wireless network. The useof multirobots for optimization of activities has been expanding9 and in10a multi-area cooperative cover is applied to develop complex tasks wheremultiple robots are required to cover together in several areas. Practicaloperations such as garbage removal, demining and area scanning for infor-mation acquisition.11Based on this information a work was developed with the objectiveof constructing a group of robots in which the agents are able to sharecleaning workloads automatically and in an intelligent way. In addition, itis programmed to detect obstacles around it and control the robot to avoidcollisions. The metology adopted is detailed in the following section.3. MethodologyIn this article, the game engine, Unity 3D, was used as the simulationenvironment for natively presenting robust features for the development ofthe12 simulation environment, as shown in figure 1.The figure 1 (b) represents the area of action and division of the map-ping, in quadrants of 50 x 50 meters and each agent is circled in blue. Atthis stage they are responsible for traversing the quadrant, analyzing andstoring the location where it contains dirt particles. The mapping route ofan agent in the lower left quadrant, in red color, is also demonstrated.Communication between the robots was done through sockets.13 As asearch algorithm for mapping, BFS14 was chosen to go through the quad-rant, we can check the route in the 1 (b) figure. Next, a navigation mesh willbe generated, figure 1 (a), and routes to the dirt points. At each frame theAugust 21, 2018 9:17 WSPC - Proceedings Trim Size: 9in x 6in ws-procs9x6442(a) (b)Figure 1.agents calculate (algorithm 3.1) the closest point, this point must belongto only one agent.A navigation mesh (NavMesh) has been created that consists of severaltwo-dimensional convex polygons and defines which areas of an environ-ment are likely to be traversed by agents. In other words, an agent canmove within these unobstructed areas of the environment and Agents in aNavMesh can avoid collision detection checks with obstacles in the environ-ment.15NavMesh allows a large number of algorithms to search for graphics,and the algorithm discussed in this work was Pathfinding A* in each of theagents.16 In the figure 1 (a) in blue, represents the finished mesh.The algorithm Pathfinding A* is an algorithm capable of finding theshortest path,17 it uses a function to determine which squares to traverse,according to equation 1.f(n) = g(n) + h(n) (1)Where g is the cost of moving from a starting point to a given square,17oh (n) is the cost estimate of the ideal path from node n to destination.173.1. Control AlgorithmA priori is divided into two parts, mapping and cleaning. The BFS is thealgorithm responsible for guiding the next point that the agent must followin the mapping phase. The points were divided in the quadrant area andallowed to travel in a larger area. In order to perform the cleaning, twoAugust 21, 2018 9:17 WSPC - Proceedings Trim Size: 9in x 6in ws-procs9x6443algorithms were used, one for navigation Pathfinding A* and the other,algorithm 3.1, for point selection. A test without the algorithm 3.1 was alsoperformed to evaluate the performance of the algorithm.In conjunction with the algorithms, a decision method was proposed,which evaluates the most advantageous point that the agent should move,to carry out the cleaning. It calculates the distance of all mapped pointsand takes the closest one. Subsequently, the distance of the other agents iscalculated to the chosen point, if only the agent in question is the shortestdistance, is moved to the point, otherwise it picks up another nearby pointand remakes the calculation. The method described is represented in the3.1 algorithm.The actions are shown in the flowchart, Figure 2, demonstrates theoperation of the two steps described, mapping and cleaning, it is possibleto verify that the agent always checks if there are still points that needcleaning, the algorithm 3.1.Algorithm 3.1 Method of decision1: procedure NextPoint(agent, point)2: a← ShorterDistanceThisAgent3: b← ShorterDistanceOthesAgents4: if a < b then return nextPoint . Check if it’s the closest5: else return nextInitial . Return to starting point4. ResultsIn this section we present the performance of the algorithms, mapping andcontrol. In the tables 1 and 2, the time each agent finished cleaning, theamount of dirt points collected and the distance traveled are represented.Based on the results obtained, represented in the tables 1 e 2, the algo-rithm 3.1 presents a gain in time and distance, representing a differentialof 91.46 seconds faster and a saving of 482.77 meters.The use of the BFS mapping algorithm in conjunction with PathfindingA* has been shown to be efficient, since the function of BFS is to ensurethat the search is linear, but since the scenario has the presence of obsta-cles it was necessary to integrate the two for better efficiency. In the testsperformed the agents were able to map the area and find all points.August 21, 2018 9:17 WSPC - Proceedings Trim Size: 9in x 6in ws-procs9x6444Figure 2. Control software flowchartTable 1.Agent Time Collected dirt Travelled distance5000 29.24 s 42 241.45 m5001 35.45 s 49 295.57 m5002 34.48 s 37 319.65 m5003 27.16 s 40 242.17 mTotal 126.33 s 167 1098.84 mNote: Pathfinding A*, algorithm 1August 21, 2018 9:17 WSPC - Proceedings Trim Size: 9in x 6in ws-procs9x6445Table 2.Agent Time Collected dirt Travelled distance5000 54.61 s 36 367.47 m5001 54.52 s 39 438.39 m5002 53.97 s 36 393.44 m5003 54.69 s 56 382.31 mTotal 217.79 s 167 1581.61 mNote: Pathfinding A*, Euclidian Distance5. ConclusionIn this article we have demonstrated the use of algorithms already knownin the literature in the aid of autonomous robotics, the agents were able toclean, map, route routes and avoid obstacles. Because it is more than oneagent, a combination was proposed in the algorithm of navigation Pathfind-ing A * with algorithm of decision based on Euclidean distance, as a resultobtained an improvement in the time performance and distance traveled.AcknowledgmentsThis work is financed by the ERDF - European Regional DevelopmentFund through the Operational Programme for Competitiveness and Inter-nationalization - COMPETE 2020 Program within project POCI-01-0145-FEDER-006961,and by National Funds through the FCT - Fundac¸a˜o para aCieˆncia e a Tecnologia (Portuguese Foundation for Science and Technology)as part of project UID/EEA/50014/2013.Bibliography1. M. S. Couceiro, N. M. Fonseca Ferreira and R. Rocha, Multi-robot explo-ration based on swarm optimization algorithms, in 7th European NonlinearDynamics Conference ENOC , 2011.2. M. S. Couceiro, R. P. Rocha and N. M. Fonseca Ferreira, A novel multi-robot exploration approach based on particle swarm optimization algorithms,in 2011 IEEE International Symposium on Safety, Security, and RescueRobotics, Nov 2011.3. K. R. Muske, H. Ashrafiuon, G. Haas, R. McCloskey and T. Flynn, Iden-tification of a control oriented nonlinear dynamic usv model, in AmericanControl Conference, 2008 , 2008.4. F. Y. Lee, B. H. Jun, P. M. Lee and K. Kim, Implementation and test ofisimi100 auv for a member of auvs fleet, in OCEANS 2008 , 2008.5. J. Wang, W. Gu, J. Zhu and J. Zhang, An unmanned surface vehicle for multi-mission applications, in Electronic Computer Technology, 2009 InternationalConference on, 2009.August 21, 2018 9:17 WSPC - Proceedings Trim Size: 9in x 6in ws-procs9x64466. W. H. C. Wickramaarachchi, M. A. P. Chamikara and R. A. C. H. Rat-nayake, Towards implementing efficient autonomous vacuum cleaning sys-tems, in 2017 IEEE International Conference on Industrial and InformationSystems (ICIIS), Dec 2017.7. H. 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Scalable Task Cleanup Assignment for Multi-agents

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