Resilient Navigation through Probabilistic Modality Reconfiguration

Peynot, Thierry, Fitch, Robert, McAllister, Rowan, & Alempijevic, Alen (2013) Resilient Navigation through Probabilistic Modality Reconfiguration. In Lee, Sukhan, Cho, Hyungsuck, Yoon, Kwang-Joon, & Lee, Jangmyung (Eds.) Intelligent Autonomous Systems 12. Springer-Verlag, Berlin, pp. 75-88.

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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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ID Code: 67612
Item Type: Book Chapter
Keywords: mobile robots, navigation, modality reconfiguration
DOI: 10.1007/978-3-642-33932-5_8
ISBN: 9783642339318
ISSN: 2194-5357
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Springer-Verlag Berlin Heidelberg
Deposited On: 06 Mar 2014 01:12
Last Modified: 14 Dec 2015 05:45

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