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Resilient Navigation through Probabilistic Modality Reconfiguration

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Intelligent Autonomous Systems 12

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 194))

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Abstract

This paper proposes an approach to achieve resilient navigation for indoor mobile robots. Resilient navigation seeks to mitigate the impact of control, localisation, or map errors on the safety of the platform while enforcing the robot’s ability to achieve its goal. We show that resilience to unpredictable errors can be achieved by combining the benefits of independent and complementary algorithmic approaches to navigation, or modalities, each tuned to a particular type of environment or situation. In this paper, the modalities comprise a path planning method and a reactive motion strategy. While the robot navigates, a Hidden Markov Model continually estimates the most appropriate modality based on two types of information: context (information known a priori) and monitoring (evaluating unpredictable aspects of the current situation). The robot then uses the recommended modality, switching between one and another dynamically. Experimental validation with a SegwayRMP-based platform in an office environment shows that our approach enables failure mitigation while maintaining the safety of the platform. The robot is shown to reach its goal in the presence of: 1) unpredicted control errors, 2) unexpected map errors and 3) a large injected localisation fault.

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Author information

Authors and Affiliations

  1. Australian Centre for Field Robotics, The University of Sydney, Sydney, NSW, 2006, Australia

    Thierry Peynot, Robert Fitch & Rowan McAllister

  2. Mechatronics and Intelligent Systems Group, University of Technology Sydney, Sydney, NSW, 2007, Australia

    Alen Alempijevic

Authors
  1. Thierry Peynot
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  2. Robert Fitch
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  3. Rowan McAllister
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  4. Alen Alempijevic
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Corresponding author

Correspondence to Thierry Peynot .

Editor information

Editors and Affiliations

  1. , School of Information and Communication, Sungkyunkwan University, 300 Chunchun-Dong Jangan-Ku, Kyunggi-Do, 440-746, Korea, Republic of (South Korea)

    Sukhan Lee

  2. Science and Technology, Daegu Gyeongbuk Institute of, 50-1, Sang-Ri, Hyeonpung-Myeon, Dalseong-Gun, Daegu, 711-873, Korea, Republic of (South Korea)

    Hyungsuck Cho

  3. Konkuk University, Neungdong-ro, Gwangjin-gu 120, Seoul, 143-701, Korea, Republic of (South Korea)

    Kwang-Joon Yoon

  4. , Department of Electronics Engineering, Pusan National University, Geum Jeong-ku, Pusan, 609-735, Korea, Republic of (South Korea)

    Jangmyung Lee

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Peynot, T., Fitch, R., McAllister, R., Alempijevic, A. (2013). Resilient Navigation through Probabilistic Modality Reconfiguration. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33932-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-33932-5_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33931-8

  • Online ISBN: 978-3-642-33932-5

  • eBook Packages: EngineeringEngineering (R0)

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