Abstract
Environmental boundary estimation is the process of bounding the region(s) where the measurement of all locations exceeds a certain threshold value. In this paper, we develop a framework for environmental boundary tracking and estimation in partially observable environments which are processed in an online manner. Dedicated sensors mounted on the vehicle are considered to be capable of on-the-spot field intensity measurements. Focusing on the limited resources of Unmanned Aerial Vehicles (UAVs), it is important to track an unknown boundary in a fast manner. Therefore, we present a motion planning strategy that enables a single UAV to estimate the boundary of a given target area while minimizing the exploration cost. To do so, we improve the conventional position controller based framework by integrating a noise canceling filter and a novel adaptive crossing angle correction scheme. The effectiveness of the proposed algorithm is demonstrated in three different simulated environments. We also analyze the performance of framework subject to various conditions.
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Environmental boundary tracking, vehicles, estimation using multiple autonomous. Proceedings of the IEEE Conference on Decision and Control pp 4918–4923 (2007)
Baronov, D., Baillieul, J.: Reactive exploration through following isolines in a potential field. pp. 2141–2146 IEEE (2007)
Casbeer, D.W., Kingston, D.B., Beard, R.W., McLain, T.W.: Cooperative forest fire surveillance using a team of small unmanned air vehicles. Int. J. Systems Science 37, 351–360 (2006)
Fahad, M., Saul, N., Guo, Y., Bingham, B.: Robotic Simulation of Dynamic Plume Tracking by Unmanned Surface Vessels. In: IEEE International Conference on Robotics and Automation, pp 2654–2659 (2015)
Gokaraju, B., Durbha, S.S., King, R.L., Younan, N.H.: Sensor Web and Data Mining Approaches for Harmful Algal Bloom Detection and Monitoring in the Gulf of Mexico Region. In: IEEE International Geoscience & Remote Sensing Symposium, pp 789–792 (2009)
Gotovos, A., Casati, N., Hitz, G., Krause, A.: Active Learning for Level Set Estimation. In: Proceedings of the 23Rd International Joint Conference on Artificial Intelligence. IJCAI / AAAI, pp 1344–1350 (2013)
Hsieh, M.y.A.: Stabilization of multiple robots on stable orbits via local sensing stabilization of multiple robots on stable orbits via local sensing (April) (2007)
Joshi, A., Ashley, T., Huang, Y.R., Bertozzi, A.L.: Experimental Validation of Cooperative Environmental Boundary Tracking with On-Board Sensors. In: 2009 American Control Conference. IEEE, pp 2630–2635 (2009)
Kemp, M., Bertozzi, A., Marthaler, D.: Multi-Uuv Perimeter Surveillance. In: Autonomous Underwater Vehicles. IEEE, pp 102–107 (2004)
Kurvinen, K., Smolander, P., Pöllänen, R., Kuukankorpi, S., Kettunen, M., Lyytinen, J.: Design of a radiation surveillance unit for an unmanned aerial vehicle. J. Environ. Radioact. 81(1), 1–10 (2005)
Li, S., Guo, Y., Bingham, B.: Multi-Robot Cooperative Control for Monitoring and Tracking Dynamic Plumes. In: IEEE International Conference on Robotics and Automation, pp 67–73 (2014)
Marthaler, D., Bertozzi, A.L.: Tracking environmental level sets with autonomous vehicles. J. Electrochem. Soc. 129, 2865 (2003)
Matveev, A.S., Hoy, M.C., Ovchinnikov, K., Anisimov, A., Savkin, A.V.: Robot navigation for monitoring unsteady environmental boundaries without field gradient estimation. Automatica 62, 227–235 (2015)
Matveev, A.S., Teimoori, H., Savkin, A.V.: Method for tracking of environmental level sets by a unicycle-like vehicle. Automatica 48, 2252–2261 (2012)
McClure, D.E., Vitale, R.A.: Polygonal approximation of plane convex bodies. J. Math. Anal. Appl. 51, 326–358 (1975)
Newaz, A.A.R., Jeong, S., Lee, H., Ryu, H., Chong, N.Y.: Uav-based multiple source localization and contour mapping of radiation fields. Robot. Auton. Syst. 85, 12–25 (2016)
Newaz, A.A.R., Jeong, S., Lee, H., Ryu, H., Chong, N.Y., Mason, M.T.: Fast radation mapping and multiple source localization using topographic contour map and incremental density estimation. In: IEEE International Conference on Robotics and Automation pp. 1515–1521
Salda, D., Assunc R.: Predicting Environmental Boundary Behaviors with a Mobile Robot (2016)
Soltero, D.E., Schwager, M., Rus, D.: Decentralized path planning for coverage tasks using gradient descent adaptive control. Int. J. Robot. Res. 33, 401–425 (2014)
Sun, T., Pei, H., Pan, Y., Zhang, C.: Robust adaptive neural network control for environmental boundary tracking by mobile robots. Int. J. Robust Nonlinear Control 23, 123–136 (2013)
Susca, S., Bullo, F., Martinez, S.: Monitoring environmental boundaries with a robotic sensor network. IEEE Trans. Control Syst. Technol. 16, 288–296 (2008)
Towler, J., Krawiec, B., Kochersberger, K.: Terrain and radiation mapping in post-disaster environments using an autonomous helicopter. Remote Sens. 4, 1995–2015 (2012)
Wang, Z., Wu, J., Yang, J., Lin, H.: Optimal Energy Efficient Level Set Estimation of Spatially-Temporally Correlated Random Fields. In: International Conference on Communications. IEEE, pp 1–6 (2016)
White, B., Tsourdos, A., Ashokoraj, I., Subchan, S., Zbikowski, R.: Contaminant Cloud Boundary Monitoring Using Uav Sensor Swarms. AIAA Journal of Guidance, Control, and Dynamics (2005)
Willett, R., Nowak, R.: Minimax optimal level set estimation. IEEE Trans. Image Process. 16(12), 2965–2979 (2007)
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This work was supported by the Industrial Convergence Core Technology Development Program (No. 10063172) funded by MOTIE, Korea.
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Newaz, A.A.R., Jeong, S. & Chong, N.Y. Online Boundary Estimation in Partially Observable Environments Using a UAV. J Intell Robot Syst 90, 505–514 (2018). https://doi.org/10.1007/s10846-017-0664-9
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DOI: https://doi.org/10.1007/s10846-017-0664-9