Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search

Due to growing endurance, safety and non-invasivity, small drones can be increasingly experimented in unstructured environments. Their moderate computing power can be assimilated into swarm coordination algorithms, performing tasks in a scalable manner. For this purpose, it is challenging to investigate the use of biologically-inspired mechanisms. In this paper the focus is on the coordination aspects between small drones required to perform target search. We show how this objective can be better achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone senses a potential target, by releasing digital pheromone on its location. Multiple pheromone deposits are aggregated, increasing in intensity, but also diffused, to be propagated to neighborhood, and lastly evaporated, decreasing intensity in time. As a consequence, pheromone intensity creates a spatiotemporal attractive potential field coordinating a swarm of drones to visit a potential target. Flocking occurs when drones are spatially organized into groups, whose members have approximately the same heading, and attempt to remain in range between them, for each group. It is an emergent effect of individual rules based on alignment, separation and cohesion. In this paper, we present a novel and fully decentralized model for target search, and experiment it empirically using a multi-agent simulation platform. The different combination strategies are reviewed, describing their performance on a number of synthetic and real-world scenarios

Using Differential Evolution to Improve Pheromone-based Coordination of Swarms of Drones for Collaborative Target Detection

In this paper we propose a novel algorithm for adaptive coordination of drones, which performs collaborative target detection in unstructured environments. Coordination is based on digital pheromones released by drones when detecting targets, and maintained in a virtual environment. Adaptation is based on the Differential Evolution (DE) and involves the parametric behaviour of both drones and environment. More precisely, attractive/repulsive pheromones allow indirect communication between drones in a flock, concerning the availability/unavailability of recently found targets. The algorithm is effective if structural parameters are properly tuned. For this purpose DE combines different parametric solutions to increase the swarm performance. We focus first on the study of the principal parameters of the DE, i.e., the crossover rate and the differential weight. Then, we compare the performance of our algorithm with three different strategies on six simulated scenarios. Experimental results show the effectiveness of the approach

Connectivity-Aware Pheromone Mobility Model for Autonomous UAV Networks

UAV networks consisting of reduced size, weight, and power (low SWaP) fixed-wing UAVs are used for civilian and military applications such as search and rescue, surveillance, and tracking. To carry out these operations efficiently, there is a need to develop scalable, decentralized autonomous UAV network architectures with high network connectivity. However, the area coverage and the network connectivity requirements exhibit a fundamental trade-off. In this paper, a connectivity-aware pheromone mobility (CAP) model is designed for search and rescue operations, which is capable of maintaining connectivity among UAVs in the network. We use stigmergy-based digital pheromone maps along with distance-based local connectivity information to autonomously coordinate the UAV movements, in order to improve its map coverage efficiency while maintaining high network connectivity

Implementasi Algoritma PSO Pada Multi Mobile Robot Dalam Penentuan Posisi Target Terdekat

 Swarm Intelligence is an artificial intelligence developed by adapting the social behavior of a group of animal. In the migratory birds community, it is known that the behavior of the birds during the flight forms a 'V' formation that plays a role in optimizing the bird's energy saving. The basic principle of a swarm intelligence is the existence of collective, decentralized and self-organizing behavior. This is the basis for the development of behavioral algorithms flocking birds called Particle Swarm Optimization (PSO).In this research used three mobile robot as object to implement PSO algorithm. Three pieces of this robot is homogeneous, which is similar hardware and software. A group of these robots will complete the joint mission of defining the robot with the closest distance to the target TPr (robot handler). There are three TPr targets that have to be executed by the robot handler according to their position with the target point to be completed. The test is done by taking odometry data every 250 milisekon and data frame robot communication.At the end of this research, the result of modeling system result of PSO algorithm implementation on mobile robot group to determine the robot closest to the target. The robot system that meets the principles of PSO, namely the process of data sharing and learning process

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Practical applications using multi-UAV systems and aerial robotic swarms

A día de hoy, existen en el mercado una gran cantidad de aeronaves sin piloto que pueden ser comandadas con órdenes de alto nivel para realizar tareas complejas de forma casi automática, como por ejemplo el mapeo de explotaciones agrícolas. De forma natural, nos podemos preguntar si sería posible coordinar a un grupo de estos robots para realizar esas mismas tareas de forma más rápida, flexible y robusta. En este trabajo se repasan las tareas que se han planteado resolver con sistemas compuestos por grupos de aeronaves no tripuladas y los algoritmos empleados, así como los métodos y estrategias en los que están basados. Aunque el futuro de estos sistemas es prometedor, existen ciertos obstáculos legislativos y técnicos que frenan su implantación de forma generalizadaLas investigaciones que han dado como resultado este trabajo han sido financiadas por RoboCity2030-DIH-CM, 426 Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, financiadas por los Programas de Actividades I+D en la Comunidad Madrid, y por el proyecto TASAR (Team of AdvancedSearch And Rescue Robots), PID2019-105808RB-I00, financiado por el Ministerio de Ciencia e Innovación (Gobierno deEspaña

Practical applications using multi-UAV systems and aerial robotic swarms

[EN] Nowadays, there are a large number of unmanned aircraft on the market that can be commanded with high-level orders to perform complex tasks almost automatically, such as mapping crop fields. We can ask ourselves if it would be possible to coordinate a group of these robots to perform those same tasks more quickly, flexibly and robustly. In this work, we summarize the tasks that have been studied to be solved with systems composed by groups of unmanned aircraft and the algorithms used, as well as the methods and strategies on which they are based. Although the future of these systems is promising, there are certain legislative and technical obstacles that stop their implementation in a generalized way.[ES] A día de hoy, existen en el mercado una gran cantidad de aeronaves sin piloto que pueden ser comandadas con ordenes de alto nivel para realizar tareas complejas de forma casi automatica, como por ejemplo el mapeo de explotaciones agrícolas. De forma natural, nos podemos preguntar si sería posible coordinar a un grupo de estos robots para realizar esas mismas tareas de forma más rápida, flexible y robusta. En este trabajo se repasan las tareas que se han planteado resolver con sistemas compuestos por grupos de aeronaves no tripuladas y los algoritmos empleados, así como los metodos y estrategias en los que están basados. Aunque el futuro de estos sistemas es prometedor, existen ciertos obstaculos legislativos y técnicos que frenan su implantación de forma generalizada.Las investigaciones que han dado como resultado este trabajo han sido financiadas por RoboCity2030-DIH-CM, 426 Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, financiadas por los Programas de Actividades I+D en la Comunidad Madrid, y por el proyecto TASAR (Team of Advanced Search And Rescue Robots), PID2019-105808RB-I00, financiado por el Ministerio de Ciencia e Innovacion (Gobierno de España).García-Aunon, P.; Roldán, J.; De León, J.; Del Cerro, J.; Barrientos, A. (2021). Aplicaciones practicas de los sistemas multi-UAV y enjambres aéreos. Revista Iberoamericana de Automática e Informática industrial. 18(3):230-241. https://doi.org/10.4995/riai.2020.13560OJS230241183Acevedo, J. J., Arrue, B. C., Maza, I., Ollero, A., 2013. Cooperative large area surveillance with a team of aerial mobile robots for long endurance missions. Journal of Intelligent & Robotic Systems 70 (1-4), 329-345. https://doi.org/10.1007/s10846-012-9716-3Albani, D., IJsselmuiden, J., Haken, R., Trianni, V., 2017. Monitoring and mapping with robot swarms for agricultural applications. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, pp. 1-6. https://doi.org/10.1109/AVSS.2017.8078478Alvear, O., Zema, N. R., Natalizio, E., Calafate, C. T., 2017. Using uav-based systems to monitor air pollution in areas with poor accessibility. Journal of Advanced Transportation 2017. https://doi.org/10.1155/2017/8204353Augugliaro, F., Lupashin, S., Hamer, M., Male, C., Hehn, M., Mueller, M. W., Willmann, J. S., Gramazio, F., Kohler, M., D'Andrea, R., 2014. The flight assembled architecture installation: Cooperative construction with flying machines. IEEE Control Systems Magazine 34 (4), 46-64. https://doi.org/10.1109/MCS.2014.2320359Barrientos, A., Colorado, J., Cerro, J. d., Martinez, A., Rossi, C., Sanz, D., Valente, J., 2011. Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots. Journal of Field Robotics 28 (5), 667-689. https://doi.org/10.1002/rob.20403Beck, Z., Teacy, W. L., Rogers, A., Jennings, N. R., 2018. Collaborative online planning for automated victim search in disaster response. Robotics and Autonomous Systems 100, 251-266. https://doi.org/10.1016/j.robot.2017.09.014Bennet, D. J., MacInnes, C., Suzuki, M., Uchiyama, K., 2011. Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles. Journal of guidance, control, and dynamics 34 (6), 1899-1908. https://doi.org/10.2514/1.53931Bernard, M., Kondak, K., Maza, I., Ollero, A., 2011. Autonomous transportation and deployment with aerial robots for search and rescue missions. Journal of Field Robotics 28 (6), 914-931. https://doi.org/10.1002/rob.20401Carrasco, Á. M., Novoa, S. C., Al-Kaff, A., Fernández, F. G., Gómez, D. M., de la Escalera Hueso, A., 2020. Vehículo aéreo no tripulado para vigilancia y monitorización de incendios. Revista Iberoamericana de Automática e Informática industrial.Chen, S., Li, C., Zhuo, S., 2017. A distributed coverage algorithm for multiuav with average voronoi partition. In: 2017 17th International Conference on Control, Automation and Systems (ICCAS). IEEE, pp. 1083-1086. https://doi.org/10.23919/ICCAS.2017.8204377Cieslewski, T., Choudhary, S., Scaramuzza, D., 2018. Data-efficient decentralized visual slam. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 2466-2473. https://doi.org/10.1109/ICRA.2018.8461155Cimino, M. G., Lazzeri, A., Vaglini, G., 2015. Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search. In: 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, pp. 1-6. https://doi.org/10.1109/IISA.2015.7387990Cledat, E., Cucci, D., 2017. Mapping gnss restricted environments with a drone tandem and indirect position control. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4, 1. https://doi.org/10.5194/isprs-annals-IV-2-W3-1-2017Cole, D. T., Thompson, P., Göktogan, A. H., Sukkarieh, S., 2010. System development and demonstration of a cooperative uav team for mapping and tracking. The International Journal of Robotics Research 29 (11), 1371-1399. https://doi.org/10.1177/0278364910364685Darrah, M., Trujillo, M. M., Speransky, K., Wathen, M., 2017. Optimized 3d mapping of a large area with structures using multiple multirotors. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 716-722. https://doi.org/10.1109/ICUAS.2017.7991414Erignac, C., 2007. An exhaustive swarming search strategy based on distributed pheromone maps. In: AIAA Infotech@ Aerospace 2007 Conference and Exhibit. p. 2822. https://doi.org/10.2514/6.2007-2822Fu, Z., Chen, Y., Ding, Y., He, D., 2019. Pollution source localization based on multi-uav cooperative communication. IEEE Access 7, 29304-29312. https://doi.org/10.1109/ACCESS.2019.2900475Fujisawa, R., Imamura, H., Hashimoto, T., Matsuno, F., 2008. Communication using pheromone field for multiple robots. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 1391-1396. https://doi.org/10.1109/IROS.2008.4650971Garcia-Aunon, P., Barrientos, A., 2018a. Comparison of heuristic algorithms in discrete search and surveillance tasks using aerial swarms. Applied Sciences 8 (5), 711. https://doi.org/10.3390/app8050711Garcia-Aunon, P., Barrientos, A., 2018b. Control optimization of an aerial robotic swarm in a search task and its adaptation to different scenarios. Journal of computational science 29, 107-118. https://doi.org/10.1016/j.jocs.2018.10.004Garcia-Aunon, P., del Cerro, J., Barrientos, A., 2019a. Behavior-based control for an aerial robotic swarm in surveillance missions. Sensors 19 (20), 4584. https://doi.org/10.3390/s19204584Garcia-Aunon, P., Roldan, J. J., Barrientos, A., 2019b. Monitoring traffic in future cities with aerial swarms: Developing and optimizing a behavior-based surveillance algorithm. Cognitive Systems Research 54, 273-286. https://doi.org/10.1016/j.cogsys.2018.10.031Garnier, S., Tache, F., Combe, M., Grimal, A., Theraulaz, G., 2007. Alice in pheromone land: An experimental setup for the study of ant-like robots. In: 2007 IEEE Swarm Intelligence Symposium. IEEE, pp. 37-44. https://doi.org/10.1109/SIS.2007.368024George, J., Sujit, P., Sousa, J. B., 2011. Search strategies for multiple uav search and destroy missions. Journal of Intelligent & Robotic Systems 61 (1-4), 355-367. https://doi.org/10.1007/s10846-010-9486-8Hadaegh, F. Y., Chung, S.-J., Manohara, H. M., 2014. On development of 100- gram-class spacecraft for swarm applications. IEEE Systems Journal 10 (2), 673-684. https://doi.org/10.1109/JSYST.2014.2327972Han, J., Xu, Y., Di, L., Chen, Y., 2013. Low-cost multi-uav technologies for contour mapping of nuclear radiation field. Journal of Intelligent & Robotic Systems 70 (1-4), 401-410. https://doi.org/10.1007/s10846-012-9722-5Hauert, S., Winkler, L., Zufferey, J.-C., Floreano, D., 2008. Ant-based swarming with positionless micro air vehicles for communication relay. Swarm Intelligence 2 (2-4), 167-188. https://doi.org/10.1007/s11721-008-0013-5Hinzmann, T., Stastny, T., Conte, G., Doherty, P., Rudol, P., Wzorek, M., Galceran, E., Siegwart, R., Gilitschenski, I., 2016. Collaborative 3d reconstruction using heterogeneous uavs: System and experiments. In: International Symposium on Experimental Robotics. Springer, pp. 43-56. https://doi.org/10.1007/978-3-319-50115-4_5Ju, C., Son, H., 2018. Multiple uav systems for agricultural applications: control, implementation, and evaluation. Electronics 7 (9), 162. https://doi.org/10.3390/electronics7090162Kim, J. H., Kwon, J.-W., Seo, J., 2014. Multi-uav-based stereo vision system without gps for ground obstacle mapping to assist path planning of ugv. Electronics Letters 50 (20), 1431-1432. https://doi.org/10.1049/el.2014.2227Lanillos, P., Gan, S. K., Besada-Portas, E., Pajares, G., Sukkarieh, S., 2014. Multi-uav target search using decentralized gradient-based negotiation with expected observation. Information Sciences 282, 92-110. https://doi.org/10.1016/j.ins.2014.05.054Li, W., 2015. Persistent surveillance for a swarm of micro aerial vehicles by flocking algorithm. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 229 (1), 185-194. https://doi.org/10.1177/0954410014529100Lyu, Y., Pan, Q., Zhang, Y., Zhao, C., Zhu, H., Tang, T., Liu, L., 2015. Simultaneously multi-uav mapping and control with visual servoing. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 125-131. https://doi.org/10.1109/ICUAS.2015.7152283Mahdoui, N., Frémont, V., Natalizio, E., 2017. Cooperative exploration strategy for micro-aerial vehicles fleet. In: 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, pp. 180-185. https://doi.org/10.1109/MFI.2017.8170426Maza, I., Ollero, A., 2007. Multiple uav cooperative searching operation using polygon area decomposition and efficient coverage algorithms. In: Distributed Autonomous Robotic Systems 6. Springer, pp. 221-230. https://doi.org/10.1007/978-4-431-35873-2_22Mirjan, A., Gramazio, F., Kohler, M., Augugliaro, F., D'Andrea, R., 2013. Architectural fabrication of tensile structures with flying machines. Green Design, Materials and Manufacturing Processes, 513-518. https://doi.org/10.1201/b15002-99Niedzielski, T., Jurecka, M., Mizinski, B., Remisz, J., Slopek, J., Spallek, W., Witek-Kasprzak, M., Kasprzak, Ł., Swierczynska-Chlasciak, M., 2018. A real-time field experiment on search and rescue operations assisted by unmanned aerial vehicles. Journal of Field Robotics 35 (6), 906-920. https://doi.org/10.1002/rob.21784Nigam, N., Bieniawski, S., Kroo, I., Vian, J., 2011. Control of multiple uavs for persistent surveillance: algorithm and flight test results. IEEE Transactions on Control Systems Technology 20 (5), 1236-1251. https://doi.org/10.1109/TCST.2011.2167331Odonkor, P., Ball, Z., Chowdhury, S., 2019. Distributed operation of collaborating unmanned aerial vehicles for time-sensitive oil spill mapping. Swarm and Evolutionary Computation 46, 52-68. https://doi.org/10.1016/j.swevo.2019.01.005Oh, S.-H., Suk, J., 2010. Evolutionary design of the controller for the search of area with obstacles using multiple uavs. In: ICCAS 2010. IEEE, pp. 2541- 2546. https://doi.org/10.1109/ICCAS.2010.5670230Perez-Carabaza, S., Besada-Portas, E., Lopez-Orozco, J. A., Jesus, M., 2018. Ant colony optimization for multi-uav minimum time search in uncertain domains. Applied Soft Computing 62, 789-806. https://doi.org/10.1016/j.asoc.2017.09.009Qu, Y., Zhang, Y., Zhang, Y., 2015. A uav solution of regional surveillance based on pheromones and artificial potential field theory. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 380-385. https://doi.org/10.1109/ICUAS.2015.7152313Rastgoftar, H., Atkins, E. M., 2018. Cooperative aerial lift and manipulation (calm). Aerospace Science and Technology 82, 105-118. https://doi.org/10.1016/j.ast.2018.09.005Reina, D., Tawfik, H., Toral, S., 2018. Multi-subpopulation evolutionary algorithms for coverage deployment of uav-networks. Ad Hoc Networks 68, 16-32. https://doi.org/10.1016/j.adhoc.2017.09.005Reuder, J., Jonassen, M. O., Olafsson, H., 2012. The small unmanned meteorological observer sumo: Recent developments and applications of a micro-uas for atmospheric boundary layer research. Acta Geophysica 60 (5), 1454- 1473. https://doi.org/10.2478/s11600-012-0042-8Reynolds, C. W., 1987. Flocks, herds and schools: A distributed behavioral model. Vol. 21. ACM. https://doi.org/10.1145/37402.37406Roldan, J. J., Garcia-Aunon, P., Peña-Tapia, E., Barrientos, A., 2019. Swarm-city project: Can an aerial swarm monitor traffic in a smart city? In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, pp. 862-867. https://doi.org/10.1109/PERCOMW.2019.8730677Santamaria, E., Segor, F., Tchouchenkov, I., 2013. Rapid aerial mapping with multiple heterogeneous unmanned vehicles. In: ISCRAM. Citeseer.Saska, M., Chudoba, J., Preucil, L., Thomas, J., Loianno, G., Tresnak, A., Vonasek, V., Kumar, V., 2014. Autonomous deployment of swarms of microaerial vehicles in cooperative surveillance. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 584-595. https://doi.org/10.1109/ICUAS.2014.6842301Savkin, A. V., Huang, H., 2019. Asymptotically optimal deployment of drones for surveillance and monitoring. Sensors 19 (9), 2068. https://doi.org/10.3390/s19092068Schilling, F., Lecoeur, J., Schiano, F., Floreano, D., 2018. Learning visionbased cohesive flight in drone swarms. arXiv preprint arXiv:1809.00543.Schmuck, P., Chli, M., 2017. Multi-uav collaborative monocular slam. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 3863-3870. https://doi.org/10.1109/ICRA.2017.7989445Silic, M. B., Song, Z., Mohseni, K., 2018. Anisotropic flocking control of distributed multi-agent systems using fluid abstraction. In: 2018 AIAA Information Systems-AIAA Infotech@ Aerospace. p. 2262. https://doi.org/10.2514/6.2018-2262Sreenath, K., Kumar, V., 2013. Dynamics, control and planning for cooperative manipulation of payloads suspended by cables from multiple quadrotor robots. rn 1 (r2), r3. https://doi.org/10.15607/RSS.2013.IX.011St-Onge, D., Kaufmann, M., Panerati, J., Ramtoula, B., Cao, Y., Coffey, E. B., Beltrame, G., 2019. Planetary exploration with robot teams. IEEE Robotics & Automation Magazine.Stavros, E. N., Agha, A., Sirota, A., Quadrelli, M., Ebadi, K., Yun, K., 2019. Smoke sky-exploring new frontiers of unmanned aerial systems for wildland fire science and applications. arXiv preprint arXiv:1911.08288.Techy, L., Schmale III, D. G., Woolsey, C. A., 2010. Coordinated aerobiological sampling of a plant pathogen in the lower atmosphere using two autonomous unmanned aerial vehicles. Journal of Field Robotics 27 (3), 335-343. https://doi.org/10.1002/rob.20335Tuna, G., Nefzi, B., Conte, G., 2014. Unmanned aerial vehicle-aided communications system for disaster recovery. Journal of Network and Computer Applications 41, 27-36. https://doi.org/10.1016/j.jnca.2013.10.002Twidwell, D., Allen, C. R., Detweiler, C., Higgins, J., Laney, C., Elbaum, S., 2016. Smokey comes of age: unmanned aerial systems for fire management. Frontiers in Ecology and the Environment 14 (6), 333-339. https://doi.org/10.1002/fee.1299Vasarhelyi, G., Viragh, C., Somorjai, G., Tarcai, N., Szorenyi, T., Nepusz, T., Vicsek, T., 2014. Outdoor flocking and formation flight with autonomous aerial robots. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 3866-3873. https://doi.org/10.1109/IROS.2014.6943105Verdu, T., Hattenberger, G., Lacroix, S., 2019. Flight patterns for clouds exploration with a fleet of uavs. https://doi.org/10.1109/ICUAS.2019.8797953Waharte, S., Trigoni, N., 2010. Supporting search and rescue operations with uavs. In: 2010 International Conference on Emerging Security Technologies. IEEE, pp. 142-147. https://doi.org/10.1109/EST.2010.31Wang, Z., Singh, S., Pavone, M., Schwager, M., 2018. Cooperative object transport in 3d with multiple quadrotors using no peer communication. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1064-1071. https://doi.org/10.1109/ICRA.2018.8460742Zhao, N., Lu, W., Sheng, M., Chen, Y., Tang, J., Yu, F. R., Wong, K.-K., 2019. Uav-assisted emergency networks in disasters. IEEE Wireless Communications 26 (1), 45-51. https://doi.org/10.1109/MWC.2018.1800160Zheng, X., Wang, F., Li, Z., 2018. A multi-uav cooperative route planning methodology for 3d fine-resolution building model reconstruction. ISPRS journal of photogrammetry and remote sensing 146, 483-494. https://doi.org/10.1016/j.isprsjprs.2018.11.00

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations