Abstract
This paper presents the application of a minimalistic navigation strategy, based on the well-known BUG2 algorithm, to solve the problem of reaching a goal position in a multi-floor indoor scenario using a quadrotor. Examples of this scenario include buildings and in general cluttered indoor areas. As far as energy backup is concerned the quadrotor shows stricts constraints: for this reason implementing a low-consumption navigation strategy is a major issue. We present a two-layer navigation strategy, called MF-BUG2, useful to navigate in multi-floor buildings starting from the ground floor toward the last or vice versa while searching for an interesting physical quantity (i.e,. gas leak, electromagnetic source). In the lower layer a BUG-like algorithm is able to drive the flying robot, equipped with a salient-cue sensor and a laser-range-finder, toward the estimated position of goal on the horizontal plane while avoiding obstacles and using minimal computational power and memory (the boundary-following behavior uses an Artificial Potential Field to navigate around the obstacles). If the estimated goal position is reached but the salient-cue-sensor does not detect a salient quantity the higher level of the planner calls Dijkstra algorithm to computes the minimum-distance path to change the floor, assuming to know in advance the 2D position of the passages among different floors, and then moves vertically. The overall strategy is usefull for indoor inspection in hazardous scenarios. The algorithm is validated in simulation, investigating the robustness with respect to the laser-range-finder noise.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Funded by the Italian Ministry of Research and Education.
- 2.
- 3.
- 4.
- 5.
References
S. Shen, Y. Mulgaonkar, N. Michael, and V. Kumar, Multi-Sensor Fusion for Robust Autonomous Flight in Indoor and Outdoor Environments with a Rotorcraft MAV, in Proc. of the IEEE Intl. Conf. on Robot. and Autom., 2014. Submitted
Michael N., Shen S., Mohta K., Mulgaonkar Y., Kumar V., Nagatani K., Okada Y., Kiribayashi S., Otake K., Yoshida K., Ohno K., Takeuchi E., and Tadokoro S., Collaborative mapping of an earthquake-damaged building via ground and aerial robots, J. Field Robotics, vol. 29, no. 5, pp. 832–841, 2012
Shen S., Michael N., Kumar V.: Autonomous Multi-floor Indoor Navigation with a Computational Constrained MAV, ICRA 2011
Mastrogiovanni F., Sgorbissa A., Zaccaria R.: Robust Navigation in an Unknown Environment With Minimal Sensing and Representation, IEEE Transactions on Systems, Man and Cybernetics, vol. 39, Feb 2009
Franz M., Mallot H.: Biomimetic robot navigation, Robotics and Autonomous Systems 30, 2000
Lumelsky V., Stepanov A.: Path-Planning Strategies for a Point Mobile Automaton Moving Amidst Unknown Obstacles of Arbitrary Shape, Algorithmica 1987
Trullier O., Wiener S., Berhoz A., Meyer J., Biologically-based artifical navigation systems: Review and prospects, Prog. Neurobiol. vol. 51, 1997
Tang K., Li C. and Chiu S.: An Electronic-Nose Sensor Node Based on a Polymer-Coated Surface Acoustic Wave Array for Wireless Sensor Network Applications, Sensors 2011
Gonzalez J., Blanco J.L., Galindo C., Ortiz-de-Galisteo A., Fernandez-Madrigal J.A., Moreno F.A., and Martinez J.L.: Mobile Robot Localization based on Ultra-Wide-Band Ranging: A Particle Filter Approach
Choset H., Lynch K., Hutchinson S., Kantor G., Burgard W., Kavraki L., Thrun S., Principles of Robot Motion, Cambridge, MA: MIT Press 2005
LaValle S.: Planning Algorithms, Cambridge Univ. Press 2006
Zhu Y., Zhang T., Song J., Li X.: A New Bug-type Navigation Algorithm Considering Practical Implementation Issues for Mobile Robots, IEEE International Conference on Robotics and Biomimetics, 2010
Antich J, Ortiz A., Minguez J.: A Bug-Inspired Algorithm for Efficient Anytime Path Planning, IEEE International Conference on Intelligent Robots and Systems, 2009
Lumelsky V., Tiwari S.: An Algorithm for Maze Searching with Azimuth Input, IEEE 1994
Kamon I., Rimon E., Rivilin E.: TangentBug: A Range-Sensor-Based Navigation Algorithm, IJRR 1998
Laubach S.L: Theory and Experiments in Autonomous Sensor-Based Motion Planning with Application for Flight Planetary Microrovers, Ph.D. Thesis, Caltech 1999
Taylor K., LaValle S.: I-Bug: An intensity-based bug algorithm. In Proceedings IEEE International Conference on Robotics and Automation, 2009
Kutulakos K., Lumelsky V., Dyer C.: Vision-Guided Exploration: A Step Toward General Motion Planning in Three Dimensions, Int. Conf. on Robotics and Automation 1993
Kutulakos K., Dyer C., Lumelsky V.: Provable Strategies for Vision-Guided Exploration in Three Dimensions, IEEE International Conference on Robotics and Automation, 1994
Kamon I., Rimon E., Rivilin E.: Range-Sensor-Based Navigation in Three-Dimensional Polyhedral Environments, The International Journal of Robotics Research, January 2001
Marino R.: Design, Modeling and Control of a Multicopter Flying Robot. M.Sc. Thesis, Ecole Centrale de Nantes, Sept 2012
Acknowledgments
This research is in the context of project PRISMA (PON04a2-A) granted by the Italian Ministry of Research and Education (MIUR).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Marino, R., Mastrogiovanni, F., Sgorbissa, A., Zaccaria, R. (2016). A Minimalistic Quadrotor Navigation Strategy for Indoor Multi-floor Scenarios. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_112
Download citation
DOI: https://doi.org/10.1007/978-3-319-08338-4_112
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08337-7
Online ISBN: 978-3-319-08338-4
eBook Packages: EngineeringEngineering (R0)