Space-Time Continuous Models of Swarm Robotic Systems: Supporting Global-to-Local Programming

A generic model in as far as possible mathematical closed-form was developed that predicts the behavior of large self-organizing robot groups (robot swarms) based on their control algorithm. In addition, an extensive subsumption of the relatively young and distinctive interdisciplinary research field of swarm robotics is emphasized. The connection to many related fields is highlighted and the concepts and methods borrowed from these fields are described shortly

Systems and algorithms for wireless sensor networks based on animal and natural behavior

In last decade, there have been many research works about wireless sensor networks (WSNs) focused on improving the network performance as well as increasing the energy efficiency and communications effectiveness. Many of these new mechanisms have been implemented using the behaviors of certain animals, such as ants, bees, or schools of fish.These systems are called bioinspired systems and are used to improve aspects such as handling large-scale networks, provide dynamic nature, and avoid resource constraints, heterogeneity, unattended operation, or robustness, amongmanyothers.Therefore, thispaper aims to studybioinspired mechanisms in the field ofWSN, providing the concepts of these behavior patterns in which these new approaches are based. The paper will explain existing bioinspired systems in WSNs and analyze their impact on WSNs and their evolution. In addition, we will conduct a comprehensive review of recently proposed bioinspired systems, protocols, and mechanisms. Finally, this paper will try to analyze the applications of each bioinspired mechanism as a function of the imitated animal and the deployed application. Although this research area is considered an area with highly theoretical content, we intend to show the great impact that it is generating from the practical perspective.Sendra, S.; Parra Boronat, L.; Lloret, J.; Khan, S. (2015). Systems and algorithms for wireless sensor networks based on animal and natural behavior. International Journal of Distributed Sensor Networks. 2015:1-19. doi:10.1155/2015/625972S1192015Iram, R., Sheikh, M. I., Jabbar, S., & Minhas, A. A. (2011). Computational intelligence based optimization in wireless sensor network. 2011 International Conference on Information and Communication Technologies. doi:10.1109/icict.2011.5983561Lloret, J., Bosch, I., Sendra, S., & Serrano, A. (2011). A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing. Sensors, 11(6), 6165-6196. doi:10.3390/s110606165Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification. Sensors, 9(11), 8722-8747. doi:10.3390/s91108722Dasgupta, P. (2008). A Multiagent Swarming System for Distributed Automatic Target Recognition Using Unmanned Aerial Vehicles. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 38(3), 549-563. doi:10.1109/tsmca.2008.918619Quwaider, M., & Biswas, S. (2012). Delay Tolerant Routing Protocol Modeling for Low Power Wearable Wireless Sensor Networks. Network Protocols and Algorithms, 4(3). doi:10.5296/npa.v4i3.2054Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Liu, M., & Song, C. (2012). Ant-Based Transmission Range Assignment Scheme for Energy Hole Problem in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 8(12), 290717. doi:10.1155/2012/290717Riva, G., & Finochietto, J. M. (2012). Pheromone-based In-Network Processing for Wireless Sensor Network Monitoring Systems. Network Protocols and Algorithms, 4(4). doi:10.5296/npa.v4i4.2206Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3Kim, J.-Y., Sharma, T., Kumar, B., Tomar, G. S., Berry, K., & Lee, W.-H. (2014). Intercluster Ant Colony Optimization Algorithm for Wireless Sensor Network in Dense Environment. International Journal of Distributed Sensor Networks, 10(4), 457402. doi:10.1155/2014/457402Dressler, F., & Akan, O. B. (2010). A survey on bio-inspired networking. Computer Networks, 54(6), 881-900. doi:10.1016/j.comnet.2009.10.024Atakan, B., & Akan, O. B. (2006). Immune System Based Distributed Node and Rate Selection in Wireless Sensor Networks. 2006 1st Bio-Inspired Models of Network, Information and Computing Systems. doi:10.1109/bimnics.2006.361806Di Pietro, R., & Verde, N. V. (2011). Introducing epidemic models for data survivability in Unattended Wireless Sensor Networks. 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks. doi:10.1109/wowmom.2011.5986165Marwaha, S., Indulska, J., & Portmann, M. (2009). Biologically Inspired Ant-Based Routing in Mobile Ad hoc Networks (MANET): A Survey. 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing. doi:10.1109/uic-atc.2009.95Jha, V., Khetarpal, K., & Sharma, M. (2011). A survey of nature inspired routing algorithms for MANETs. 2011 3rd International Conference on Electronics Computer Technology. doi:10.1109/icectech.2011.5942042Fernandez-Marquez, J. L., Di Marzo Serugendo, G., Montagna, S., Viroli, M., & Arcos, J. L. (2012). Description and composition of bio-inspired design patterns: a complete overview. Natural Computing, 12(1), 43-67. doi:10.1007/s11047-012-9324-yCamilo, T., Carreto, C., Silva, J. S., & Boavida, F. (2006). An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks. Lecture Notes in Computer Science, 49-59. doi:10.1007/11839088_5Selvakennedy, S., Sinnappan, S., & Shang, Y. (2006). T-ANT: A Nature-Inspired Data Gathering Protocol for Wireless Sensor Networks. Journal of Communications, 1(2). doi:10.4304/jcm.1.2.22-29Almshreqi, A. M. S., Ali, B. M., Rasid, M. F. A., Ismail, A., & Varahram, P. (2012). An improved routing mechanism using bio-inspired for energy balancing in wireless sensor networks. The International Conference on Information Network 2012. doi:10.1109/icoin.2012.6164367Chen, G., Guo, T.-D., Yang, W.-G., & Zhao, T. (2006). An improved ant-based routing protocol in Wireless Sensor Networks. 2006 International Conference on Collaborative Computing: Networking, Applications and Worksharing. doi:10.1109/colcom.2006.361893Okdem, S., & Karaboga, D. (2006). Routing in Wireless Sensor Networks Using Ant Colony Optimization. First NASA/ESA Conference on Adaptive Hardware and Systems (AHS’06). doi:10.1109/ahs.2006.63Salehpour, A.-A., Mirmobin, B., Afzali-Kusha, A., & Mohammadi, S. (2008). An energy efficient routing protocol for cluster-based wireless sensor networks using ant colony optimization. 2008 International Conference on Innovations in Information Technology. doi:10.1109/innovations.2008.4781748Wen, Y., Chen, Y., & Pan, M. (2008). Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics. Journal of Zhejiang University-SCIENCE A, 9(4), 531-538. doi:10.1631/jzus.a071382Liao, W.-H., Kao, Y., & Wu, R.-T. (2011). Ant colony optimization based sensor deployment protocol for wireless sensor networks. Expert Systems with Applications, 38(6), 6599-6605. doi:10.1016/j.eswa.2010.11.079Pavai, K., Sivagami, A., & Sridharan, D. (2009). Study of Routing Protocols in Wireless Sensor Networks. 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies. doi:10.1109/act.2009.133Juan, L., Chen, S., & Chao, Z. (2007). Ant System Based Anycast Routing in Wireless Sensor Networks. 2007 International Conference on Wireless Communications, Networking and Mobile Computing. doi:10.1109/wicom.2007.603Wang, C., & Lin, Q. (2008). Swarm intelligence optimization based routing algorithm for Wireless Sensor Networks. 2008 International Conference on Neural Networks and Signal Processing. doi:10.1109/icnnsp.2008.4590326Jiang, H., Wang, M., Liu, M., & Yan, J. (2012). A quantum-inspired ant-based routing algorithm for WSNs. Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD). doi:10.1109/cscwd.2012.6221881Okazaki, A. M., & Frohlich, A. A. (2011). Ant-based Dynamic Hop Optimization Protocol: A routing algorithm for Mobile Wireless Sensor Networks. 2011 IEEE GLOBECOM Workshops (GC Wkshps). doi:10.1109/glocomw.2011.6162356Hui, X., Zhigang, Z., & Xueguang, Z. (2009). A Novel Routing Protocol in Wireless Sensor Networks Based on Ant Colony Optimization. 2009 International Conference on Environmental Science and Information Application Technology. doi:10.1109/esiat.2009.460AbdelSalam, H. S., & Olariu, S. (2012). BEES: BioinspirEd backbonE Selection in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 23(1), 44-51. doi:10.1109/tpds.2011.100Da Silva Rego, A., Celestino, J., dos Santos, A., Cerqueira, E. C., Patel, A., & Taghavi, M. (2012). BEE-C: A bio-inspired energy efficient cluster-based algorithm for data continuous dissemination in Wireless Sensor Networks. 2012 18th IEEE International Conference on Networks (ICON). doi:10.1109/icon.2012.6506592Neshat, M., Sepidnam, G., Sargolzaei, M., & Toosi, A. N. (2012). Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 42(4), 965-997. doi:10.1007/s10462-012-9342-2Antoniou, P., Pitsillides, A., Blackwell, T., & Engelbrecht, A. (2009). Employing the flocking behavior of birds for controlling congestion in autonomous decentralized networks. 2009 IEEE Congress on Evolutionary Computation. doi:10.1109/cec.2009.4983153Ruihua, Z., Zhiping, J., Xin, L., & Dongxue, H. (2011). Double cluster-heads clustering algorithm for wireless sensor networks using PSO. 2011 6th IEEE Conference on Industrial Electronics and Applications. doi:10.1109/iciea.2011.5975688Kulkarni, R. V., Venayagamoorthy, G. K., & Cheng, M. X. (2009). Bio-inspired node localization in wireless sensor networks. 2009 IEEE International Conference on Systems, Man and Cybernetics. doi:10.1109/icsmc.2009.5346107Kulkarni, R. V., & Venayagamoorthy, G. K. (2010). Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 663-675. doi:10.1109/tsmcc.2010.2049649Xin Song, Cuirong Wang, Wang, J., & Bin Zhang. (2010). A hierarchical routing protocol based on AFSO algorithm for WSN. 2010 International Conference On Computer Design and Applications. doi:10.1109/iccda.2010.5541265Gao, X. Z., Wu, Y., Zenger, K., & Huang, X. (2010). A Knowledge-Based Artificial Fish-Swarm Algorithm. 2010 13th IEEE International Conference on Computational Science and Engineering. doi:10.1109/cse.2010.49Wang, L., & Ma, L. (2011). A hybrid artificial fish swarm algorithm for Bin-packing problem. Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. doi:10.1109/emeit.2011.6022829Yiyue, W., Hongmei, L., & Hengyang, H. (2012). Wireless Sensor Network Deployment Using an Optimized Artificial Fish Swarm Algorithm. 2012 International Conference on Computer Science and Electronics Engineering. doi:10.1109/iccsee.2012.453Yang, X.-S. (2010). A New Metaheuristic Bat-Inspired Algorithm. Studies in Computational Intelligence, 65-74. doi:10.1007/978-3-642-12538-6_6Goyal, S., & Patterh, M. S. (2013). Performance of BAT Algorithm on Localization of Wireless Sensor Network. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 6(3), 351-358. doi:10.24297/ijct.v6i3.4481Krishnanand, K. N., & Ghose, D. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems, 2(3), 209-222. doi:10.3233/mgs-2006-2301Apostolopoulos, T., & Vlachos, A. (2011). Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem. International Journal of Combinatorics, 2011, 1-23. doi:10.1155/2011/523806Liao, W.-H., Kao, Y., & Li, Y.-S. (2011). A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks. Expert Systems with Applications, 38(10), 12180-12188. doi:10.1016/j.eswa.2011.03.053Sun, Y., Jiang, Q., & Zhang, K. (2012). A clustering scheme for Reachback Firefly Synchronicity in wireless sensor networks. 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content. doi:10.1109/icnidc.2012.6418705Zungeru, A. M., Ang, L.-M., & Seng, K. P. (2012). Termite-Hill. International Journal of Swarm Intelligence Research, 3(4), 1-22. doi:10.4018/jsir.2012100101KumarE, S., S. M., K., & Kumar B. P., V. (2014). Clustering Protocol for Wireless Sensor Networks based on Rhesus Macaque (Macaca mulatta) Animal's Social Behavior. International Journal of Computer Applications, 87(8), 20-27. doi:10.5120/15229-3754Breza, M., & McCann, J. A. (2008). Lessons in Implementing Bio-inspired Algorithms on Wireless Sensor Networks. 2008 NASA/ESA Conference on Adaptive Hardware and Systems. doi:10.1109/ahs.2008.72Aziz, N. A. B. A., Mohemmed, A. W., & Sagar, B. S. D. (2007). Particle Swarm Optimization and Voronoi diagram for Wireless Sensor Networks coverage optimization. 2007 International Conference on Intelligent and Advanced Systems. doi:10.1109/icias.2007.4658528Falcon, R., Li, X., Nayak, A., & Stojmenovic, I. (2012). A harmony-seeking firefly swarm to the periodic replacement of damaged sensors by a team of mobile robots. 2012 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2012.6363859Antoniou, P., & Pitsillides, A. (2010). A bio-inspired approach for streaming applications in wireless sensor networks based on the Lotka–Volterra competition model. Computer Communications, 33(17), 2039-2047. doi:10.1016/j.comcom.2010.07.020Benahmed, K., Merabti, M., & Haffaf, H. (2012). Inspired Social Spider Behavior for Secure Wireless Sensor Networks. International Journal of Mobile Computing and Multimedia Communications, 4(4), 1-10. doi:10.4018/jmcmc.2012100101Alrajeh, N. A., & Lloret, J. (2013). Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(10), 351047. doi:10.1155/2013/351047Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic Algorithm for Hierarchical Wireless Sensor Networks. Journal of Networks, 2(5). doi:10.4304/jnw.2.5.87-97Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks. Fourth International Conference on Information Technology (ITNG’07). doi:10.1109/itng.2007.97Ferentinos, K. P., & Tsiligiridis, T. A. (2007). Adaptive design optimization of wireless sensor networks using genetic algorithms. Computer Networks, 51(4), 1031-1051. doi:10.1016/j.comnet.2006.06.013Jia, J., Chen, J., Chang, G., & Tan, Z. (2009). Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Computers & Mathematics with Applications, 57(11-12), 1756-1766. doi:10.1016/j.camwa.2008.10.036Nan, G.-F., Li, M.-Q., & Li, J. (2007). Estimation of Node Localization with a Real-Coded Genetic Algorithm in WSNs. 2007 International Conference on Machine Learning and Cybernetics. doi:10.1109/icmlc.2007.4370265Saleem, K., Fisal, N., Abdullah, M. S., Zulkarmwan, A. B., Hafizah, S., & Kamilah, S. (2009). Proposed Nature Inspired Self-Organized Secure Autonomous Mechanism for WSNs. 2009 First Asian Conference on Intelligent Information and Database Systems. doi:10.1109/aciids.2009.75Jabbari, A., & Lang, W. (2010). Advanced Bio-inspired Plausibility Checking in a Wireless Sensor Network Using Neuro-immune Systems: Autonomous Fault Diagnosis in an Intelligent Transportation System. 2010 Fourth International Conference on Sensor Technologies and Applications. doi:10.1109/sensorcomm.2010.24Ponnusamy, V., & Abdullah, A. (2010). Biologically Inspired (Botany) Mobile Agent Based Self-Healing Wireless Sensor Network. 2010 Sixth International Conference on Intelligent Environments. doi:10.1109/ie.2010.46Li, J., Cui, Z., & Shi, Z. (2012). An Improved Artificial Plant Optimization Algorithm for Coverage Problem in WSN. Sensor Letters, 10(8), 1874-1878. doi:10.1166/sl.2012.2627Sendra, S., Llario, F., Parra, L., & Lloret, J. (2014). Smart Wireless Sensor Network to Detect and Protect Sheep and Goats to Wolf Attacks. Recent Advances in Communications and Networking Technology, 2(2), 91-101. doi:10.2174/22117407112016660012Sendra, S., Granell, E., Lloret, J., & Rodrigues, J. J. P. C. (2013). Smart Collaborative Mobile System for Taking Care of Disabled and Elderly People. Mobile Networks and Applications, 19(3), 287-302. doi:10.1007/s11036-013-0445-zGarcia, M., Sendra, S., Lloret, G., & Lloret, J. (2011). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, 5(12), 1682-1690. doi:10.1049/iet-com.2010.0654Sendra, S., Lloret, J., Rodrigues, J. J. P. C., & Aguiar, J. M. (2013). Underwater Wireless Communications in Freshwater at 2.4 GHz. IEEE Communications Letters, 17(9), 1794-1797. doi:10.1109/lcomm.2013.072313.131214Lloret, J., Sendra, S., Ardid, M., & Rodrigues, J. J. P. C. (2012). Underwater Wireless Sensor Communications in the 2.4 GHz ISM Frequency Band. Sensors, 12(4), 4237-4264. doi:10.3390/s12040423

Design and Development of an Automated Mobile Manipulator for Industrial Applications

This thesis presents the modeling, control and coordination of an automated mobile manipulator. A mobile manipulator in this investigation consists of a robotic manipulator and a mobile platform resulting in a hybrid mechanism that includes a mobile platform for locomotion and a manipulator arm for manipulation. The structural complexity of a mobile manipulator is the main challenging issue because it includes several problems like adapting a manipulator and a redundancy mobile platform at non-holonomic constraints. The objective of the thesis is to fabricate an automated mobile manipulator and develop control algorithms that effectively coordinate the arm manipulation and mobility of mobile platform. The research work starts with deriving the motion equations of mobile manipulators. The derivation introduced here makes use of motion equations of robot manipulators and mobile platforms separately, and then integrated them as one entity. The kinematic analysis is performed in two ways namely forward & inverse kinematics. The motion analysis is performed for various WMPs such as, Omnidirectional WMP, Differential three WMP, Three wheeled omni-steer WMP, Tricycle WMP and Two steer WMP. From the obtained motion analysis results, Differential three WMP is chosen as the mobile platform for the developed mobile manipulator. Later motion analysis is carried out for 4-axis articulated arm. Danvit-Hartenberg representation is implemented to perform forward kinematic analysis. Because of this representation, one can easily understand the kinematic equation for a robotic arm. From the obtained arm equation, Inverse kinematic model for the 4-axis robotic manipulator is developed. Motion planning of an intelligent mobile robot is one of the most vital issues in the field of robotics, which includes the generation of optimal collision free trajectories within its work space and finally reaches its target position. For solving this problem, two evolutionary algorithms namely Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) are introduced to move the mobile platform in intelligent manner. The developed algorithms are effective in avoiding obstacles, trap situations and generating optimal paths within its unknown environments. Once the robot reaches its goal (within the work space of the manipulator), the manipulator will generate its trajectories according to task assigned by the user. Simulation analyses are performed using MATLAB-2010 in order to validate the feasibility of the developed methodologies in various unknown environments. Additionally, experiments are carried out on an automated mobile manipulator. ATmega16 Microcontrollers are used to enable the entire robot system movement in desired trajectories by means of robot interface application program. The control program is developed in robot software (Keil) to control the mobile manipulator servomotors via a serial connection through a personal computer. To support the proposed control algorithms both simulation and experimental results are presented. Moreover, validation of the developed methodologies has been made with the ER-400 mobile platform

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Methods for the Efficient Deployment and Coordination of Swarm Robotic Systems

Swarming has been observed in many animal species, including fish, birds, insects and mammals. These biological observations have inspired mathematical models of distributed coordination that have been applied to the development of multi-agent robotic systems, such as collections of unmanned autonomous vehicles (UAVs). The advantages of a swarming approach to distributed coordination are clear: each agent acts according to a simple set of rules that can be implemented on resource-constrained devices, and so it becomes feasible to replicate agents in order to build more resilient systems. However, there remain significant challenges in making the approach practicable. This thesis addresses two of the most significant: coordination and scalability. New coordination algorithms are proposed here, all of which manage the problem of scalability by requiring only local proximity sensing between agents, without the need for any other communications infrastructure. A major source of inefficiency in the deployment of a swarm is ‘oscillation’: small movements of agents that arise as a side effect of the application of their rules but which are not strictly necessary in order to satisfy the overall system function. The thesis introduces a new metric for ‘oscillation’ that allows it to be identified and measured in swarm control algorithms. A new perimeter detection mechanism is introduced and applied to the coordination of goal-based swarms. The mechanism is used to improve the internal coordination of agents whilst maintaining a directional focus to the swarm; this is then analysed using the new metric. A mechanism is proposed to allow a swarm to exhibit a ‘healing’ behaviour by identifying internal perimeter edges (doughnuts) and then altering the movement of agents, based upon a simple criterion, to remove the holes; this also has the emergent effect of smoothing the outer edges of a swarm and creating a more uniform swarm structure. Area coverage is an important requirement in many swarm applications. Two new, efficient area-filling techniques are introduced here and exit conditions are identified to determine when a swarm has filled an area. In summary, the thesis makes significant contributions to the analysis and design of efficient control algorithms for the coordination of large scale swarms