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Mapping, localization and motion planning in mobile multi-robotic systems
Published online by Cambridge University Press: 09 February 2012
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As researchers have pushed the limits of what can be accomplished by a single robot operating in a known or unknown environment, a greater emphasis has been placed on the utilization of mobile multi-robotic systems to accomplish various objectives. In transitioning from a robot-centric approach to a system-centric approach, considerations must be made for the computational and communicative aspects of the group as a whole, in addition to electromechanical considerations of individual robots. This paper reviews the state-of-the-art of mobile multi-robotic system research, with an emphasis on the confluence of mapping, localization and motion control of robotic system. Methods that compose these three topics are presented, including areas of overlap, such as integrated exploration and simultaneous localization and mapping. From these methods, an analysis of benefits, challenges and tradeoffs associated with multi-robotic system design and use are presented. Finally, specific applications of multi-robotic systems are also addressed in various contexts.
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References
1.Makarenko, A. A., Williams, S. B., Bourgault, F. and Durrant-Whyte, H. F., “An Experiment in Integrated Exploration,” In: Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Switzerland (Sep.–Oct. 2002) pp. 534–539.Google Scholar
2.Howard, A., Matarić, M. J. and Sukhatme, G. S., “Localization for Mobile Robot Teams Using Maximum Likelihood Estimation,” In: Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Switzerland (Sep.–Oct. 2002) pp. 434–439.Google Scholar
3.Amigoni, F., Caglioti, V. and Fontana, G., “A Perceptive Multirobot System for Monitoring Electro-Magnetic Fields,” Proceedings of IEEE Symposium on Virtual Environments, Hunan-Computer Interfaces and Measurement Systems, Boston, Massachusetts (Jul. 2004) pp. 95–100.Google Scholar
4.Latimer, D. IV, Srinivasa, S. and Lee-Shue, V., “Towards Sensor-Based Coverage with Robot Teams,” In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation, Washington, DC (May 2002) pp. 961–967.Google Scholar
5.Savkin, A. V. and Teimoori, H., “Decentralized navigation of groups of wheeled mobile robots with limited communication,” IEEE Trans. Robot. 26, 1099–1104 (2010).CrossRefGoogle Scholar
6.Mohan, Y. and Ponnambalam, S. G., “An Extensive Review of Research in Swarm Robotics,” Proceedings of World Congress on Nature and Biologically Inspired Computing, Coimbatore, India (Dec. 2009) pp. 140–145.Google Scholar
7.Simmons, R., Singh, S., Hershberger, D., Ramos, J. and Smith, T., “First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly,” In: Proceedings of the International Symposium on Experimental Robotics, Honolulu, Hawaii (Dec. 2000) pp. 323–322.Google Scholar
8.Ben-Tzvi, P., Goldenberg, A. A. and Zu, J. W., “Design and analysis of a hybrid mobile robot mechanism with compounded locomotion and manipulation capability,” J. Mech. Des. 130, 1–13 (2008).CrossRefGoogle Scholar
9.Kim, Y. and Minor, M. A., “Distributed kinematic motion control of multi-robot coordination subject to physical constraints,” Int. J. Robot. Res. 29, 92–109 (2010).CrossRefGoogle Scholar
10.Kim, Y. and Minor, M. A., “Coordinated kinematic control of compliantly coupled multi-robot systems in an array format,” IEEE Trans. Robot. 26, 173–180 (2010).CrossRefGoogle Scholar
11.O'Grady, R., Christensen, A. L. and Dorigo, M., “SWARMORPH: multi-robot morphogenesis using directional self-assembly,” IEEE Trans. Robot. 25, 738–743 (2009).CrossRefGoogle Scholar
12.Moorehead, S. J., Simmons, R. and Whittaker, W. L., “Autonomous exploration using multiple sources of information,” In: Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea (May 2001) pp. 3098–3103.Google Scholar
13.Elfes, A., “Using occupancy grids for mobile robot perception and navigation,” IEEE Computer 22, 46–57 (1989).CrossRefGoogle Scholar
14.Bourgault, F., Makarenko, A. A., Williams, S. B., Grocholsky, B. and Durrant-Whyte, H. F., “Information-Based Adaptive Robotic Exploration,” In: Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Switzerland (Sep.–Oct. 2002) pp. 540–545.Google Scholar
15.Makarenko, A. A., Williams, S. B., Bourgault, F. and Durrant-Whyte, H. F., “Decentralized Certainty Grid Maps,” In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, Nevada (Oct. 2003) pp. 3258–3263.Google Scholar
16.Brunskill, E. and Roy, N., “SLAM using Incremental Probabilistic PCA and Dimensionality Reduction,” In: Proceedings of the IEEE/RSJ International Conference of Robotics and Automation, Barcelona, Spain (Apr. 2005) pp. 342–347.Google Scholar
17.Lee, S. J., Cho, D. W., Chung, W. K., Lim, J. H. and Kang, C. U., “Feature-Based Map Building Using Sparse Sonar Data,” In: Proceedings of the 2005 International Conference on Intelligent Robots and Systems, Edmonton, Canada (Aug. 2005) pp. 1648–1652.Google Scholar
18.González-Baños, H., Mao, E., Latombe, J. C., Murali, T. M. and Efrat, A., “Planning Robot Motion Strategies for Efficient Model Construction,” In: Proceedings of the 9th International Symposium of Robotics Research, Snowbird, Utah (Oct. 2000) pp. 345–352.Google Scholar
19.Albers, S. and Henzinger, M. R., “Exploring Unknown Environments,” In: Proceedings of the 29th Annual ACM Symposium on Theory of Computing, El Paso, Texas (May 1997) pp. 416–425.Google Scholar
20.Rocha, R., Ferreira, F. and Dias, J., “Multi-Robot Complete Exploration Using Hill Climbing and Topological Recovery,” In: Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (Sep. 2008) pp. 1884–1889.Google Scholar
21.Tarutoko, Y., Kobayashi, K. and Watanabe, K., “Topological Map Generation Based on Delaunay Triangulation for Mobile Robot,” Proceedings of the International Joint Conference of SICE-ICASE, Busan, Korea (Oct. 2006) pp. 492–496.Google Scholar
22.Hollinger, G., Singh, S., Djugash, J. and Kehagias, A., “Efficient multi-robot search for a moving target,” Int. J. Robot. Res. 28, 201–219 (2009).CrossRefGoogle Scholar
23.Rocha, R., Dias, J. and Carvaho, A., “Exploring Information Theory for Vision-Based Volumetric Mapping,” In: Proceedings of the 2005 International Conference on Intelligent Robots and Systems, Edmonton, Canada (Aug. 2005) pp. 1023–1028.Google Scholar
24.Karg, M., Wurm, K. M., Stachniss, C., Dietmayer, K. and Burgard, W., “Consistent Mapping of Multistory Buildings by Introducing Global Constraints to Graph-Based SLAM,” In: Proceedings of IEEE International Conference on Robotics and Automation, Anchorage, Alaska (May 2010) pp. 5383–5388.Google Scholar
25.Montijano, E. and Sagues, C., “Topological Maps Based on Graphs of Planar Regions,” In: Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, Missouri (Oct. 2009) pp. 1661–1666.Google Scholar
26.Bandera, A., Urdiales, C. and Sandoval, F., “An Hierarchical Approach to Grid-Based and Topological Maps Integration for Autonomous Indoor Navigation,” In: Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, Hawaii (Oct.–Nov. 2001) pp. 883–888.Google Scholar
27.Kaupp, T., Douillard, B., Upcroft, B. and Makarenko, A., “Hierarchical Environment Model for Fusing Information from Human Operators and Robots,” In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (Oct. 2006) pp. 5837–5842.Google Scholar
28.Ferreira, F., Amorim, I., Rocha, R. and Dias, J., “T-SLAM: Registering Topological and Geometric Maps for Robot Localization in Large Environments,” In: Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Seoul, Korea (2008) pp. 392–398.Google Scholar
29.Birk, A. and Carpin, S., “Merging occupancy grid maps from multiple robots,” Proc. IEEE 94, 1384–1397 (2006).CrossRefGoogle Scholar
30.Konolige, K., Fox, D., Limketkai, B., Ko, J. and Stewart, B., “Map Merging for Distributed Robot Navigation,” In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, Nevada (Oct. 2003) pp. 212–217.Google Scholar
31.Ko, J., Steward, B., Fox, D., Konolige, K. and Limketkai, B., “A Practical, Decision-Theoretic Approach to Multi-Robot Mapping and Exploration,” In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, Nevada (Oct. 2003) pp. 3232–3238.Google Scholar
32.Zhou, X. S. and Roumeliotis, S. I., “Multi-Robot SLAM with Unknown Initial Correspondence: The Robot Rendezvous Case,” In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (Oct. 2006) pp. 1785–1792.Google Scholar
33.Franchi, A., Freda, L., Oriolo, G. and Vendittelli, M., “The sensor-based random graph method for cooperative robot exploration,” IEEE/ASME Trans. Mechatron. 14, 163–175 (2009).CrossRefGoogle Scholar
34.Grisetti, G., Grzonka, S., Stachniss, C., Pfaff, P. and Burgard, W., “Efficient Estimation of Accurate Maximum Likelihood Maps in 3D,” In: Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, California (Oct.–Nov. 2007) pp. 3472–3478.Google Scholar
35.Drews, P. Jr., Núñez, P., Rocha, R., Campos, M. and Dias, J., “Novelty Detection and 3D Shape Retrieval Using Superquadrics and Multi-Scale Sampling for Autonomous Mobile Robots,” In: Proceedings of IEEE International Conference on Robotics and Automation, Anchorage, Alaska (May 2010) pp. 3635–3640.Google Scholar
36.Núñez, P., Drews, P. Jr., Rocha, R., Campos, M. and Dias, J., “Novelty Detection and 3D Shape Retrieval Based on Gaussian Mixture Models for Autonomous Surveillance Robotics,” In: Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, Missouri (Oct. 2009) pp. 4724–4730.Google Scholar
37.Ivanjko, E., Vašak, M. and Petrović, I., “Kalman Filter Theory-Based Mobile Robot Pose Tracking Using Occupancy Grid Maps,” International Conference on Control and Automation, Budapest, Hungary (Jun. 2005) pp. 869–874.Google Scholar
38.Fox, D., Burgard, W., Kruppa, H. and Thrun, S., “A probabilistic approach to collaborative multi-robot localization,” Autom. Robot. 8, 325–344 (2000).Google Scholar
39.Franchi, A., Oriolo, G. and Stegagno, P., “On the Solvability of the Mutual Localization Problem with Autonomous Position Measures,” In: Proceedings of IEEE International Conference on Robotics and Automation, Anchorage, Alaska (May 2010) pp. 3193–3199.Google Scholar
40.Dieudonné, Y., Labbani-Igbida, O. and Petit, F., “Deterministic robot-network localization is hard,” IEEE Trans. Robot. 26, 331–339 (2010).CrossRefGoogle Scholar
41.Ayanian, N. and Kumar, V., “Abstractions and Controllers for Groups of Robots in Environments with Obstacles,” In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, Alaska (May 2010) pp. 3537–3542.CrossRefGoogle Scholar
42.Banjanović-Mehmedović, L., Petrović, I. and Ivanjko, E., “Mobile Robot Localization Using Local Occupancy Grid Maps Transformations,” In: Proceedings of 12th International Power Electronics and Motion Control Conference, Portoroz, Slovenia (Aug.–Sep. 2006) pp. 1307–1312.Google Scholar
43.Armesto, L. and Tornero, J., “Robust and Efficient Mobile Robot Self-Localization Using Laser Scanner and Geometrical Maps” In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (Oct. 2006) pp. 3080–3085.Google Scholar
44.Gerkey, B. and Matarić, M., “A formal analysis and taxonomy of task allocation in multi-robot systems,” Int. J. Robot. Res. 23, 939–954 (2004).CrossRefGoogle Scholar
45.Lagoudakis, M., Berhault, M., Koenig, S., Keskinocak, P. and Kleywegt, A., “Simple Auctions with Performance Guarantees for Multi-Robot Task Allocation,” In: Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (Sep.–Oct. 2004) pp. 698–705.Google Scholar
46.Singh, A., Krause, A., Gustrin, C. and Kaiser, W., “Efficient Informative Sensing Using Multiple Robots,” J. Artif. Intell. Res. 34, 707–755 (2009).CrossRefGoogle Scholar
47.Roumeliotis, S. I. and Bekey, G. A., “Distributed Multirobot Localization,” IEEE Trans. Robot. Autom. 18, 781–795 (2002).CrossRefGoogle Scholar
48.Mourikis, A. I. and Roumeliotis, S. I., “On the treatment of relative-pose measurements for mobile robot localization,” In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida (May 2006) pp. 2277–2284.Google Scholar
49.Borges, G. A. and Aldon, M. J., “Optimal mobile robot pose estimation using geometrical maps,” IEEE Trans. Robot. Autom. 18, 87–94 (2002).CrossRefGoogle Scholar
50.Chen, H., Sun, D., Yang, J. and Chen, J., “Localization for multirobot formations in indoor environment,” IEEE/ASME Trans. Mechatron. 15, 561–574 (2010).CrossRefGoogle Scholar
51.Choset, H. and Nagatani, K., “Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization,” IEEE Trans. Robot. Autom. 17, 125–137 (2001).CrossRefGoogle Scholar
52.Odakura, V. and Costa, A. H. R., “Cooperative Multi-Robot Localization: Using Communication to Reduce Localization Error,” Proceedings of the International Conference on Informatics in Control, Automation and Robotics, Barcelona, Spain (Sep. 2005).Google Scholar
53.Doucet, A., Gordon, N. J. and Krishnamurthy, Y., “Particle filters for state estimation of jump markov linear systems,” IEEE Trans. Signal Process. 49, 613–624 (2001).CrossRefGoogle Scholar
54.Brooks, A., Makarenko, A. and Upcroft, B., “Gaussian Process Models for Sensor-Centric Robot Localization,” In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida (May 2006) pp. 56–61.Google Scholar
55.Cevher, V., “Importance Sampling,” STAT 631/ELEC 639: Graphical Models, Rice University, Houston, Texas (Nov. 17, 2008). Available at: www.ece.rice.edu/~vc3/elec633/ImportanceSampling.pdfGoogle Scholar
56.Porta, J. M., Verbeek, J. J. and Kröse, B. J. A., “Active appearance-based robot localization using stereo vision,” Autom. Robot. 18, 59–80 (2005).Google Scholar
57.Howard, A., Matarić, M. J. and Sukhatme, G. S., “Putting the ‘I’ in ‘Team’: An Ego-Centric Approach to Cooperative Localization,” In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan (Sep. 2003) pp. 868–874.Google Scholar
58.Martinson, E. B. and Dellaert, F., “Marco Polo localization,” In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan (Sep. 2003) pp. 1960–1965.Google Scholar
59.Dellaert, F., Alegre, F. and Martinson, E. B., “Intrinsic Localization and Mapping with 2 Applications: Diffusion Mapping and Marco Polo Localization,” In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan (Sep. 2003) pp. 2344–2349.Google Scholar
60.Djugash, J. and Singh, S., “A Robust Method of Localization and Mapping Using Only Range,” Proceedings of the 11th International Symposium on Experimental Robotics, Athens, Greece (Jul. 2008).Google Scholar
61.Hollinger, G., Djugash, J. and Singh, S., “Coordinated Search in Cluttered Environment using Range from Mobile Robots,” In: Proceedings of the 6th Inernational Conference on Field and Service Robotics, Chamonix, France (Jul. 2007) pp. 443–442.Google Scholar
62.Ferris, B., Fox, D. and Lawrence, N., “WiFi-SLAM Using Gaussian Process Latent Variable Models,” In: Proceedings of the 2007 International Joint Conference on Artificial Intelligence, Hyderabad, India (2007) pp. 2480–2485.Google Scholar
63.Michael, N. and Kumar, V., “Planning and control of ensembles of robots with non-holonomic constraints,” Int. J. Robot. Res. 28, 962–975 (2009).CrossRefGoogle Scholar
64.Rodríguez-Seda, E. J., Troy, J. J., Erignac, C. A., Murray, P., D. M. Stipanović and Spong, M. W., “Bilateral teleoperation of multiple mobile agents: Coordinated motion and collision avoidance,” IEEE Trans. Control Syst. Technol. 18, 984–992 (2010).CrossRefGoogle Scholar
65.Mariottini, G. L., Morbidi, F., Prattichizzo, D., Valk, N. V., Michael, N., Pappas, G. and Daniilidis, K., “Vision-based localization for leader-follower formation control,” IEEE Trans. Robot. 25, 1431–1438 (2009).CrossRefGoogle Scholar
66.Egerstedt, M. and Hu, X., “Formation constrained multi-agent control,” IEEE Trans. Robot. Autom. 17, 947–951 (2001).CrossRefGoogle Scholar
67.Lawton, J. R. T., Beard, R. W. and Young, B. J., “A decentralized approach to formation maneuvers,” IEEE Trans. Robot. Autom. 19, 933–941 (2003).CrossRefGoogle Scholar
68.Sun, D., Wang, C., Shang, W. and Feng, G., “A synchronization approach to trajectory tracking of multiple mobile robots while maintaining time-varying formations,” IEEE Trans. Robot. 25, 1074–1086 (2009).Google Scholar
69.Ganguli, A., Cortés, J. and Bullo, F., “Multirobot rendezvous with visibility sensors in nonconvex environments,” IEEE Trans. Robot. 25, 340–352 (2009).CrossRefGoogle Scholar
70.Litus, Y., Zebroski, P. and Vaughan, R. T., “A distributed heuristic for energy-efficient multirobot multiplace rendezvous,” IEEE Trans. Robot. 25, 130–135 (2009).CrossRefGoogle Scholar
71.Arkin, R. C. and Diaz, J., “Line-of-Sight Constrained Exploration for Reactive Multiagent Robotic Teams,” In: Proceedings of the 7th International Workshop on Advanced Motion Control, Maribor, Slovenia (Jul. 2002) pp. 455–461.Google Scholar
72.Amigoni, F., Basilico, N. and Gatti, N., “Finding the Optimal Strategies for Robotic Patrolling with Adversaries in Topologically Represented Environments,” In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan (May 2009) pp. 3850–3855.Google Scholar
73.Basilico, N., Gatti, N., Rossi, T., Ceppi, S. and Amigoni, F., “Extending Algorithms for Mobile Robot Patrolling in the Presence of Adversaries to more Realistic Settings,” In: Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Milan, Italy (Sep. 2009) pp. 557–564.Google Scholar
74.Choset, H., “Coverage for robotics – a survey of recent results,” Ann. Math. Artif. Intell. 31, 113–126 (2001).CrossRefGoogle Scholar
75.Luo, C. and Yang, S. X., “A Real-Time Cooperative Sweeping Strategy or Multiple Cleaning Robots,” In: Proceedings of the 2002 IEEE International Symposium on Intelligent Control, Vancouver, Canada (Oct. 2002) pp. 660–665.Google Scholar
76.Zheng, X., Koenig, S., Kempe, D. and Jain, S., “Multirobot Forest Coverage for Weighted and Unweighted Terrain,” IEEE Trans. Robot. 26, 1018–1031 (2010).CrossRefGoogle Scholar
77.Juliá, M., Gil, A., Payá, A. and Reinoso, Ó., “Local minima detection in potential field-based cooperative multi-robot exploration,” Int. J. Factor. Autom. Robot. Soft Comput. 3 (2008).Google Scholar
78.Garrido, S., Moreno, L. and Blanco, D., “Exploration and mapping using the VFM motion planner,” IEEE Trans. Instrum. Meas. 58, 2880–2892 (2009).CrossRefGoogle Scholar
79.Stipanović, D. M., Hokayem, P. F., Spong, M. W. and Šiljak, D. D., “Cooperative avoidance control for multiagent systems,” J. Dyn. Syst. Meas. Control 129, 699–707 (2007).CrossRefGoogle Scholar
80.Vannoy, J. and Xiao, J., “Real-time adaptive motion planning (RAMP) of mobile manipulators in dynamic environments with unforeseen changes,” IEEE Tran. Robot. 24, 1199–1212 (2008).CrossRefGoogle Scholar
81.Thrun, S., Koller, D., Ghahramani, Z., Durrant-Whyte, H. and Ng, A. Y., “Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results,” Proceedings of the 5th International Workshop on Algorithmic Foundations of Robotics, Nice, France (Dec. 2002).Google Scholar
82.Simmons, R., Apfelbaum, D., Burgard, W., Fox, D., Moors, M., Thrun, S. and Younes, H., “Coordination for Multi-Robot Exploration and Mapping,” In: Proceedings of the AAAI Conference on Artificial Intelligence, Austin, Texas (Jul.–Aug. 2000) pp. 852–858.Google Scholar
83.Lee, K. and Chung, W. K., “Effective maximum likelihood grid map with conflict evaluation filter using sonar sensors,” IEEE Trans. Robot. 25, 887–901 (2009).Google Scholar
84.Amigoni, F. and Gallo, A., “A Multi-Objective Exploration Strategy for Mobile Robots,” In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain (Apr. 2005) pp. 3850–3855.CrossRefGoogle Scholar
85.Koenig, S., Tovey, C. and Halliburton, W., “Greedy Mapping of Terrain,” In: Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea (May 2001) pp. 3594–3599.Google Scholar
86.Sim, R. and Roy, N., “Global A-Optimal Robot Exploration in SLAM,” In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain (Apr. 2005) pp. 662–666.Google Scholar
87.Burgard, W., Moors, M., Stachniss, C. and Schneider, F. E., “Coordinated Multi-Robot Exploration,” IEEE Trans. Robot. 21, 376–386 (2005).CrossRefGoogle Scholar
88.Lau, H., “Behavioural Approach for Multi-Robot Exploration,” Proceedings of the Australasian Conference on Robotics and Automation, Brisbane, Australia (Dec. 2003).Google Scholar
89.Connolly, C., “The Determination of Next Best Views,” In: Proceedings of the 1985 IEEE International Conference on Robotics and Automation (Mar. 1985) pp. 432–435.Google Scholar
90.González-Baños, H. H. and Latombe, J. C., “Navigation Strategies for Exploring Indoor Environments,” Int. J. Robot. Res. 21, 829–848 (2002).CrossRefGoogle Scholar
91.Oriolo, G., Vendittelli, M., Freda, L. and Troso, G., “The SRT Method: Randomized Strategies for Exploration,” In: Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, Louisiana (Apr.–May 2004) pp. 4688–4694.Google Scholar
92.Freda, L. and Oriolo, G., “Frontier-Based Probabilistic Strategies for Sensor-Based Exploration,” In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain (Apr. 2005) pp. 3881–3887.CrossRefGoogle Scholar
93.Wurm, K. M., Stachniss, C. and Burgard, W., “Coordinated Multi-Robot Exploration using a Segmentation of the Environment,” In: Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (Sep. 2008) pp. 1160–1165.Google Scholar
94.Berman, S., Halász, Á., Ani-Hsieh, M. and Kumar, V., “Optimized stochastic policies for task allocation in swarms of robots,” IEEE Trans. Robot. 25, 927–937 (2009).CrossRefGoogle Scholar
95.Dias, M., Zlot, R., Kalra, N. and Stentz, A., “Market-based multirobot coordination: A survey and analysis,” Proc. IEEE 94, 1257–1270 (Jul. 2006).CrossRefGoogle Scholar
96.Sheng, W., Yang, Q., Tan, J. and Xi, N., “Distributed multi-robot coordination in area exploration,” Robot. Autom. Syst. 54, 945–955 (2006).CrossRefGoogle Scholar
97.Dias, M. B. and Stentz, A., “A Free Market Architecture for Distributed Control of a Multirobot System,” Proceedings of the 6th International Conference on Intelligent Autonomous Systems, Venice, Italy (July 2000).Google Scholar
98.Choi, H. L., Brunet, L. and How, J. P., “Consensus-based decentralized auctions for robust task allocation,” IEEE Trans. Robot. 25, 912–926 (2009).CrossRefGoogle Scholar
99.Meskin, N., Khorasani, K. and Rabbath, C. A., “A hybrid fault detection and isolation strategy for a network of a unmanned vehicles in presence of large environmental disturbances,” IEEE Trans. Control Syst. Technol. 18, 1422–1429 (2010).Google Scholar
100.Durham, J. W., Franchi, A. and Bullo, F., “Distributed Pursuit-Evasion with Limited-Visibility Sensors via Frontier-Based Exploration,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, Alaska (May 2010) pp. 3562–3568.Google Scholar
101.Ghaderi, F. and Ahamadabadi, M. N., “A Cooperative Fault Tolerance Strategy for Distributed Object Lifting Robots,” In: Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Switzerland (Sep.–Oct. 2002) pp. 2721–2727.Google Scholar
102.Leung, K. Y. K., Barfoot, T. D. and Liu, H. H. T., “Decentralized localization of sparsely communicating robot networks: A centralized-equivalent approach,” IEEE Trans. Robot. 26, 62–77 (2010).CrossRefGoogle Scholar
103.Kalra, N., Ferguson, D. and Stentz, A., “Hoplites: A Market-Based Framework for Planned Tight Coordination in Multirobot Teams,” In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain (Apr. 2005) pp. 1170–1177.CrossRefGoogle Scholar
104.Rocha, R., Dias, J. and Carvaho, A., “Cooperative Multi-Robot Systems: A Study Of Vision-Based 3-D Mapping Using Information Theory,” In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain (Apr. 2005) pp. 384–389.CrossRefGoogle Scholar
105.Tardioli, D., Mosteo, A. R., Riazuelo, L., Villarroel, J. L. and Montano, L., “Enforcing network connectivity in robot team mission,” Int. J. Robot. Res. 29, 460–480 (2010).CrossRefGoogle Scholar
106.Zlot, R., Stentz, A., Dias, M. B. and Thayer, S., “Multi-Robot Exploration Controlled by a Market Economy,” In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation, Washington, DC (May 2002) pp. 3016–3023.Google Scholar
107.Rekleitis, I., Sim, R., Dudek, G. and Milios, E., “Collaborative Exploration for the Construction of Visual Maps,” In: Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, Hawaii (Oct.–Nov. 2001) pp. 1269–1274.Google Scholar
108.Franchi, A., Freda, L., Oriolo, G. and Vendittelli, M., “A Randomized Strategy for Cooperative Robot Exploration,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy (Apr. 2007) pp. 768–774.Google Scholar
109.Burgard, W., Moors, M., Fox, D., Simmons, R. and Thrun, S., “Collaborative Multi-Robot Exploration,” In: Proceedings of the 2000 IEEE International Conference on Robotics and Automation, San Francisco, California (Apr. 2000) pp. 476–481.Google Scholar
110.Kloetzer, M. and Belta, C., “Automatic deployment of distributed teams of robots from temporal logic motion specifications,” IEEE Trans. Robot. 26, 48–61 (2010).CrossRefGoogle Scholar
111.Frank, B., Stachniss, C., Schmedding, R., Teschner, M. and Burgard, W., “Real-World Robot Navigation Amongst Deformable Obstacles,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan (May 2009) pp. 1649–1654.Google Scholar
112.Yamaguchi, H., “A Distributed Motion Coordination Strategy for Multiple Nonholonomic Mobile Robots in Cooperative Hunting Operations,” In: Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, Nevada (Dec. 2002) pp. 2984–2991.Google Scholar
113.Moshtagh, N., Michael, N., Jadbabaie, A. and Daniilidis, K., “Vision-based, distributed control laws for motion coordination of non-holonomic robots,” IEEE Trans. Robot. 25, 851–860 (2009).CrossRefGoogle Scholar
114.Makarenko, A., Kaupp, T., Grocholsky, B. and Durrant-Whyte, H., “Human-Robot Interactions in Active Sensor Networks,” In: Proceedings of the 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan (July 2003) pp. 247–252.Google Scholar
115.Murphy, R. R., “Human-Robot Interaction in Rescue Robotics,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 34, 138–153 (2004).CrossRefGoogle Scholar
116.Hougen, D. F., Benjaafar, S., Bonney, J. C., Budenske, J. R., Dvorak, M., Gini, M., French, H., Krantz, D. G., Li, P. Y., Malver, F., Nelson, B., Papanikolopoulos, N., Rybski, P. E., Stoeter, S. A., Voyles, R. and Yesin, K. B., “A Miniature Robotic System for Reconnaissance and Surveillance,” In: Proceedings of the 2000 IEEE International Conference on Robotics and Automation, San Francisco, California (Apr. 2000) pp. 501–507.Google Scholar
117.Diaz, J. F., Stoytchev, A. and Arkin, R. C., “Exploring Unknown Structured Environments,” Proceedings of the 14th International Florida Artificial Intelligence Research Society Conference, Key West, Florida (May 2001).Google Scholar
118.Apostolopoulos, D. S., Pedersen, L., Shamah, B. N., Shillcutt, K., Wagner, M. D. and Whittaker, W. L., “Robotic Antarctic Meteorite Search: Outcomes,” In: Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea (May 2001) pp. 4174–4179.Google Scholar
119.Amigoni, F., Scadonici, S., Vaglioti, V. and Fontana, G., “Experimenting with a Robotic System for Localizing Magnetic Field Sources,” In: Proceedings of the IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems, Giardini Naxos, Italy (July 2005) pp. 44–49.Google Scholar
120.Schwager, M., Rus, D. and Slotine, J. J., “Decentralized, adaptive coverage control for networked robots,” Int. J. Robot. Res. 28, 357–375 (2009).CrossRefGoogle Scholar
121.Sugar, T. G. and Kumar, V., “Control of cooperating mobile manipulators,” IEEE Trans. Robot. Autom. 18, 94–103 (2002).CrossRefGoogle Scholar
122.Suzuki, K., Makami, S., Akita, J. and Osawa, E., “Development of Cooperative Small Mowing Robots,” In: Proceedings of the 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan (July 2003) pp. 1498–1502.Google Scholar
123.Thrun, S., Hähnel, D., Ferguson, D., Montemerlo, M., Triebel, R., Burgard, W., Baker, C., Omohundro, Z., Thayer, S. and Whittaker, W., “A System for Volumetric Robotic Mapping of Abandoned Mines,” In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan (Sept. 2003) pp. 4270–4275.Google Scholar
124.Lee, S. and Song, J., “Robust Mobile Robot Localization Using Optical Flow Sensors and Encoders,” In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation, New Orleans, Louisiana (Apr. 2004) pp. 1039–1044.Google Scholar
125.Kroetsch, D. and Clark, C., “Towards Gaussian Multi-Robot SLAM for Underwater Robotics,” Proceedings of the 2005 International Symposium on Unmanned Untethered Submersible Technology (Jul. 2005).Google Scholar
126.El-Osery, A., “EE 570: Location and Navigation. INS/GPS Integration,” Technical Report, Electrical Engineering Department, New Mexico Tech, Socorro, New Mexico, USA (April 29, 2011), available at: www.ee.nmt.edu/~elosery/spring_2011/ee570/lectures/ins-gps.pres.pdf, Accessed 26 October 2011.Google Scholar
127.Rivard, F., Bisson, J., Michaud, F. and Létourneau, D., “Ultrasonic Relative Positioning for Multi-Robot Systems,” In: Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Pasadena, California (May 2008) pp. 323–328.CrossRefGoogle Scholar
128.Cen, N., Cheng, K. and Fidan, B., “Formation Control of Robotic Swarms Based on Sonar Sensing,” In: Proceedings of the 2009 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia (Dec. 2009) pp. 31–36.Google Scholar
129.Nejad, S. and Olyaee, S., “Low-Noise High-Accuracy TOF Laser Range Finder,” Am. J. Appl. Sci. 5, 755–762 (2008).Google Scholar
130.Agrawal, M. and Konolige, K., “Real-Time Localization in Outdoor Environments Using Stereo Vision and Inexpensive GPS,” Proceedings of the 18th Inernational Conference on Pattern Recognition, Hong Kong (Aug. 2006).Google Scholar
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