Abstract
Autonomous surface vehicles (ASVs) are becoming more widely used in environmental monitoring applications. Due to the limited duration of these vehicles, algorithms need to be developed to save energy and maximize monitoring efficiency. This paper compares receding horizon path planning models for their effectiveness at collecting usable data in an aquatic environment. An adaptive receding horizon approach is used to plan ASV paths to collect data. A problem that often troubles conventional receding horizon algorithms is the path planner becoming trapped at local optima. Our proposed Jumping Horizon (J-Horizon) algorithm planner improves on the conventional receding horizon algorithm by jumping out of local optima. We demonstrate that the J-Horizon algorithm collects data more efficiently than commonly used lawnmower patterns, and we provide a proof-of-concept field implementation on an ASV with a temperature monitoring task in a lake.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The specific location of the field trials is Lake Haviland, outside of Durango, CO, located at \(37^{\circ } 31^{\prime } 55^{\prime \prime }\)N \(107^{\circ } 48^{\prime } 27^{\prime \prime }\)W.
References
Binney, J., Krause, A., Sukhatme, G.: Informative path planning for an autonomous underwater vehicle. In: IEEE International Conference on Robotics and Automation(ICRA), pp. 4791–4796. Anchorage, Alaska (2010)
Curcio, J., Leonard, J., Patrikalakis, A.: Scout—a low cost autonomous surface platform for research in cooperative autonomy. Oceans, pp 725–729 (2005)
Frolov, S., Garau, B., Bellingham, J.: Can we do better than the grid survey: optimal synoptic surveys in presence of variable uncertainty and decorrelation scales. J. Geophys. Res. Oceans 119(8), 5071–5090 (2014). doi:10.1002/2013JC009521
Gotovos, A., Casati, N., Hitz, G., Krause, A.: Active learning for level set estimation. In: International Joint Conference on Artificial Intelligence, Beijing, China (2013)
Grasmueck, M., Eberli, G.P., Viggiano, D.A., Correa, T., Rathwell, G., Luo, J.: Autonomous underwater vehicle (auv) mapping reveals coral mound distribution, morphology, and oceanography in deep water of the straits of florida. Geophys. Res. Lett. 33(23) (2006)
Hitz, G., Gotovos, A., Pomerleau, F., Garneau, M.E., Pradalier, C., Krause, A., Siegwart, R.: Fully autonomous focused exploration for robotic environmental monitoring. In: IEEE International Conference on Robotics and Automation (ICRA), pp 2658–2664 (2014). doi:10.1109/ICRA.2014.6907240
Hollinger, G., Singh, S.: Proofs and experiments in scalable, near-optimal search by multiple robots. In: Robotics: Science and Systems, June 2008
Hollinger, G.A., Sukhatme, G.: Sampling-based motion planning for robotic information gathering. In: Robotics: Science and Systems (2013)
Mora, A., Ho, C., Saripalli, S.: Analysis of adaptive sampling techniques for underwater vehicles. Auton. Robots 35(2–3), 111–122 (2013)
Schouwenaars, T., How, J., Feron, E.: Receding horizon path planning with implicit safety guarantees. In: Proceedings of the American Control Conference, vol 6, pp 5576–5581. IEEE (2004)
Stoker, C., Barch, D., Farmer, J., Flagg, M., Healy, T., Tengdin, T., Thomas, H., Schwer, K., Stakes, D.: Exploration of mono lake with an rov: a prototype experiment for the maps auv program. Autonomous Underwater Vehicle Technology AUV’96, 33–40 (1996)
Tisdale, J., Kim, Z., Hedrick, J.K.: Autonomous path planning and estimation using uavs. IEEE Robot. Autom. Mag. 16(2), 35–42 (2009)
Acknowledgments
The authors would like to thank Jonathan Nash at Oregon State University for his insightful comments and insight into the algorithmic development. A. Stuntz was supported in part by ONR grant N00014-14-1-0490. R.N. Smith was supported in part by NSF Grant DUE-1068341 and a gift from the Fort Lewis Foundation. S. Yoo, Y. Zhang, and G. Hollinger were supported in part by ONR grant N00014-14-1-0509.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Yoo, SH., Stuntz, A., Zhang, Y., Rothschild, R., Hollinger, G.A., Smith, R.N. (2016). Experimental Analysis of Receding Horizon Planning Algorithms for Marine Monitoring. In: Wettergreen, D., Barfoot, T. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-27702-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-27702-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27700-4
Online ISBN: 978-3-319-27702-8
eBook Packages: EngineeringEngineering (R0)