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Micro Aerial Vehicle Path Planning and Flight with a Multi-objective Genetic Algorithm

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Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

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Abstract

Due to its importance for robotics applications, robotic path planning has been extensively studied. Because optimal solutions can be computationally expensive, the need for good approximate solutions to such problems has led to the use of many techniques, including genetic algorithms. This paper proposes a genetic algorithm for offline path planning in a static but very general, continuous real-world environment that includes intermediate targets in addition to the final destination. The algorithm presented is distinct from others in several ways. First, it does not use crossover as this operator does not appear, in testing, to aid in efficiently finding a solution for most of the problem instances considered. Second, it uses mass extinction due to experimental evidence demonstrating its potential effectiveness for the path planning problem. Finally, the algorithm was designed for, and has been tested on, a physical micro aerial vehicle. It runs on a single-board computer mounted on the MAV, making the vehicle fully autonomous and demonstrating the viability of such a system in practice.

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Acknowledgments

The authors would like to thank Florida Southern College for financial and other support for this work, and 3DR for providing educational pricing and support of open source projects important to research, commercial, and hobbyist projects for MAVs.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Florida Southern College, Lakeland, FL, 33801, USA

    H. David Mathias & Vincent R. Ragusa

Authors
  1. H. David Mathias
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  2. Vincent R. Ragusa
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Correspondence to H. David Mathias .

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Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Mathias, H.D., Ragusa, V.R. (2018). Micro Aerial Vehicle Path Planning and Flight with a Multi-objective Genetic Algorithm. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-56994-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

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