Abstract
We present a set of algorithms for the navigation of Unmanned Ground Vehicles (UGVs) towards a set of pre-identified target nodes in coordinate-free and localization-free wireless sensor and actuator networks. The UGVs are equipped with a set of wireless listeners that provide sensing information about the potential field generated by the network of actuators. Two main navigation scenarios are considered: single-UGV, single-destination navigation and multi-UGV, multi-destination navigation. For the single-UGV, single-destination case, we present both centralized and distributed navigation algorithms. Both algorithms share a similar two-phase concept. In the first phase, the system assigns level numbers to individual nodes based on their hop distance from the target nodes. In the second phase, the UGV uses the potential field created by the network of actuators to move towards the target nodes, requiring cooperation between triplets of actuator nodes and the UGV. The hop distance to the target nodes is used to control the main moving direction while the potential field, which can be measured by listeners on the UGV, is used to determine the UGV’s movement. For the multi-UGV, multi-destination case, we present a decentralized allocation algorithm such that multiple UGVs avoid converging to the same destination. After each UGV determines its destination, the proposed navigation scheme is applied. The presented algorithms do not attempt to localize UGVs or sensor nodes and are therefore suitable for operating in GPS-free/denied environments. We also present a study of the communication complexity of the algorithms as well as simulation examples that verify the proposed algorithms and compare their performances.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Alankus, G., Atay, N., Lu, C., Bayazit, O.B.: Spatiotemporal query strategies for navigation in dynamic sensor network environments. In: Proc. IEEE International Conference on Intelligent Robots and Systems, pp. 3718–3725 (2005)
Batalin, M.A., Sukhatme, G.S.: Sensor network-based multi-robot task allocation. In: IROS ’03, vol. 2, pp. 1939–1944. Orlando, FL (2003)
Batalin, M.A., Sukhatme, G.S.: Using a sensor network for distributed multi-robot task allocation. In: ICRA’04, pp. 158–164. New Orleans, LA (2004)
Batalin, M.A., Sukhatme, G.S., Hatting, M.: Mobile robot navigation using a sensor network. In: Proc. IEEE International Conference on Robotics and Automation, vol. 1, pp. 636–641 (2003)
Bertsekas, D., Gallager, R.: Data Networks, Chap. 5.2.4. Prentice-Hall, Englewood Cliffs (1987)
Borenstein, J., Koren, Y.: The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans. Robot. Autom. 7(3), 278–288 (1991)
Buchart, J.G.: Detecting coverage holes in wireless sensor networks. Master’s thesis, Louisiana Tech University (2008)
Buragohain, C., Agrawal, D., Suri, S.: Distributed navigation algorithms for sensor networks. In: Proceedings of 25th IEEE International Conference on Computer Communications, pp. 1–10 (2006)
Chen, W., Mei, T., Liang, H., You, Z., Li, S., Meng, M.H.: Environment-map-free robot navigation based on wireless sensor networks. In: Proc. IEEE International Conference on Information Acquisition, pp. 569–573 (2007)
Chen, D., Kumar, B., Mohan, C., Mehrotra, K., Varshney, P.: In-network path planning for distributed sensor network navigation in dynamic environments. In: Proc. IEEE International Conference on Mobile Ad Hoc and Sensor Systems, pp. 511–513 (2008)
Chen, P., Chen, W., Shen, Y.: A distributed area-based guiding navigation protocol for wireless sensor networks. In: Proc. IEEE International Conference on Parallel and Distributed Systems, pp. 647–654 (2008)
Coltin, B., Veloso, M.: Mobile robot task allocation in hybrid wireless sensor networks. In: IEEE/RSJ International Conference on IROS, pp. 2932–2937. Taipei (2010)
Corke, P., Hrabar, S., Peterson, R., Rus, D., Saripalli, S., Sukhatme, G.: Deployment and connectivity repair of a sensor net with a flying robot. In: Proc. the 9th International Symposium on Experimental Robotics, pp. 333–343 (2004)
Corke, P., Peterson, R., Rus, D.: Localization and navigation assisted by cooperating networked sensors and robots. Int. J. Robot. Res. 24(9), 771–786 (2005)
Duncan, C.A., Kanno, J., Selmic, R.R.: Detecting (approximate) hole coverage areas in wireless sensor networks. In: Proc. Cyberspace Research Workshop. Shreveport, LA (2009)
Fu, S., Hou, Z., Yang, G.: An indoor navigation system for autonomous mobile robot using wireless sensor network. In: Proc. IEEE International Conference on Networking, Sensing and Control, pp. 227–232 (2009)
Garcia-Luna-Aceves, J., Murthy, S.: A path-finding algorithm for loop-free routing. IEEE/ACM Trans. Networking 5, 148–160 (1997)
Humblet, P.A.: An adaptive distributed Dijkstra shortest path algorithm. Tech. Rep., Massachusetts Institute of Technology, Laboratory for Information and Decision Systems (1988)
Kanno, J., Buchart, J., Selmic, R., Phoha, V.: Detecting coverage holes in wireless sensor networks. In: 17th Mediterranean conference on Control and Automation, pp. 452–457 (2009)
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Proc. IEEE International Conference on Robotics and Automation, vol. 2, pp. 500–505 (1985)
Király, A., Abonyi, J.: A novel approach to solve multiple traveling salesmen problem by genetic algorithm. In: Rudas, I., Fodor, J., Kacprzyk, J. (eds.) Computational Intelligence in Engineering, Studies in Computational Intelligence, vol. 313, pp. 141–151. Springer, Berlin (2010). doi:10.1007/978-3-642-15220-7_12
Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: Proc. IEEE International Conference on Robotics and Automation, pp. 1398–1404. Sacramento, CA (1991)
Lee, W., Hur, K., Eom, D.: Navigation of mobile node in wireless sensor networks without localization. In: Proc. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 1–7. Seoul (2008)
Li, Q., DeRosa, M., Rus, D.: Distributed algorithms for guiding navigation across a sensor network. In: Proc. the 9th Annual International Conference on Mobile Computing and Networking, MobiCom ’03, pp. 313–325 (2003)
Masehian, E., Amin-Naseri, M.R.: A Voronoi diagram-visibility graph-potential field compound algorithm for robot path planning. J. Robot. Syst. 21(6), 275–300 (2004)
Mercker, T., Akella, M., Alvarez, J.: Robot navigation in a decentralized landmark-free sensor network. J. Intell. Robot. Syst. 60(3–4), 553–576 (2010)
Merlin, P.: Design and analysis of distributed routing algorithms. Master’s thesis, University of California, Santa Cruz (1994)
Montestruque, L., Lemmon, M.: Csonet: a metropolitan scale wireless sensor-actuator network. In: International Workshop on Mobile Device and Urban Sensing (MODUS) (2008)
O’Hara, K.J., Walker, D.B., Balch, T.R.: Physical path planning using a pervasive embedded network. IEEE Trans. Robot. 24(3), 741–746 (2008)
Rappaport, T.: Wireless Communications: Principles & Practice. Prentice-Hall, New Jersey (1996)
Schiff, J., Kulkarni, A., Bazo, D., Duindam, V., Alterovitz, R., Song, D., Goldberg, K.: Actuator networks for navigating an unmonitored mobile robot. In: Proc. of IEEE Conference on Automation Science and Engineering, pp. 53–60. Washington, DC (2008)
Spinelli, J.: Broadcasting topology and routing information in computer networks. Master’s thesis, Massachusetts Institute of Technology (1985)
The cricket indoor location system. URL http://cricket.csail.mit.edu/
Tsitsiklis, J., Stamoulis, G.: On the average communication complexity of asynchronous distributed algorithms. J. ACM 42(2), 382–400 (1995)
Verma, A., Sawant, H., Tan, J.: Selection and navigation of mobile sensor nodes using a sensor network. In: Proc. the 3rd IEEE International Conference on Pervasive Computing and Communication, vol. 1, pp. 41–50 (2005)
Yao, J., Zhang, G., Kanno, J., Selmic, R.R.: Decentralized detection and patching of coverage holes in wireless sensor networks. In: Proc. SPIE Defense and Security, vol. 7352. Orlando, FL (2009)
Yu, Z., Jinhai, L., Guochang, G., Rubo, Z., Haiyan, Y.: An implementation of evolutionary computation for path planning of cooperative mobile robots. In: Proceedings of the 4th World Congress on Intelligent Control and Automation, 2002, vol. 3, pp. 1798–1802 (2002). doi:10.1109/WCICA.2002.1021392
Zhang, H., Hou, J.C.: Maximizing α-lifetime for wireless sensor networks. Int. J. Sen. Netw. 1, 64–71 (2006)
Zhang, T., Gruver, W., Smith, M.: Team scheduling by genetic search. In: Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, 1999. IPMM ’99, vol. 2, pp. 839–844 (1999). doi:10.1109/IPMM.1999.791495
Zhang, G., Duncan, C.A., Kanno, J., Selmic, R.R.: Unmanned ground vehicle navigation in coordinate-free and localization-free wireless sensor and actuator networks. In: Proc. 2010 IEEE Multi-conference on Systems and Control. Yokohama, Japan (2010)
Zhang, G., Duncan, C., Kanno, J., Selmic, R.R.: Distributed unmanned ground vehicle navigation in coordinate-free and localization-free wireless sensor and actuator networks. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pp. 7262–7267 (2011). doi:10.1109/CDC.2011.6160238
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, G., Duncan, C.A., Kanno, J. et al. Unmanned Ground Vehicle Navigation in Coordinate-Free and Localization-Free Wireless Sensor and Actuator Networks. J Intell Robot Syst 74, 869–891 (2014). https://doi.org/10.1007/s10846-013-9836-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-013-9836-4