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Towards Resilient Autonomous Navigation of Drones

Part of the Springer Proceedings in Advanced Robotics book series (SPAR,volume 20)

Abstract

Robots and particularly drones are especially useful in exploring extreme environments that pose hazards to humans. To ensure safe operations in these situations, usually perceptually degraded and without good GNSS, it is critical to have a reliable and robust state estimation solution. The main body of literature in robot state estimation focuses on developing complex algorithms favoring accuracy. Typically, these approaches rely on a strong underlying assumption: the main estimation engine will not fail during operation. In contrast, we propose an architecture that pursues robustness in state estimation by considering redundancy and heterogeneity in both sensing and estimation algorithms. The architecture is designed to expect and detect failures and adapt the behavior of the system to ensure safety. To this end, we present HeRO (Heterogeneous Redundant Odometry): a stack of estimation algorithms running in parallel supervised by a resiliency logic. This logic carries out three main functions: a) perform confidence tests both in data quality and algorithm health; b) re-initialize those algorithms that might be malfunctioning; c) generate a smooth state estimate by multiplexing the inputs based on their quality. The state and quality estimates are used by the guidance and control modules to adapt the mobility behaviors of the system. The validation and utility of the approach are shown with real experiments on a flying robot for the use case of autonomous exploration of subterranean environments, with particular results from the STIX event of the DARPA Subterranean Challenge.

A. Santamaria-Navarro and R. Thakker contributed equally to this manuscript.

The paper has supplementary material including a video showing experimental results of the methods presented in the paper.

Copyright 2019 California Institute of Technology. U.S. Government sponsorship acknowledged.

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Notes

  1. 1.

    https://www.subtchallenge.com.

  2. 2.

    http://www.pixhawk.org.

  3. 3.

    https://software.intel.com/en-us/realsense/d400.

  4. 4.

    https://velodyneLiDAR.com/vlp-16.html.

  5. 5.

    https://developer.qualcomm.com/hardware/qualcomm-flight-pro.

  6. 6.

    https://www.subtchallenge.com/.

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Acknowledgement

This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

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Correspondence to Angel Santamaria-Navarro .

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Santamaria-Navarro, A., Thakker, R., Fan, D.D., Morrell, B., Agha-mohammadi, Aa. (2022). Towards Resilient Autonomous Navigation of Drones. In: Asfour, T., Yoshida, E., Park, J., Christensen, H., Khatib, O. (eds) Robotics Research. ISRR 2019. Springer Proceedings in Advanced Robotics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-030-95459-8_57

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