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Robot Path Planning Using Imprecise and Sporadic Advisory Information from Humans

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Towards Autonomous Robotic Systems (TAROS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11650))

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Abstract

In environments featuring hazards (e.g., debris, holes in the ground), robot navigation can be challenging. Robot’s sensors alone might be not able to guarantee timely detection of the threats. In such situations, the presence of nearby humans can be exploited to support safe robot navigation. The human can proactively provide advisory information and issue warnings. Unfortunately, verbally expressed human’s inputs are usually quite imprecise or ambiguous when referring to spatial elements. We consider how to model the inherently imprecise and sporadic “human sensor” by using the formalism of imprecise probabilities, and how to use the model to build maps fusing robot sensor data and human inputs. Map information is used for path planning, searching for paths that balance survivability and efficiency (e.g., time). In a number of simulation scenarios we study the effectiveness of our approach compared to standard ways to build the map and perform path planning.

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Notes

  1. 1.

     Since the pile can be actually sensed to some extent by the simulated depth sensor, we have added Gaussian white-noise for the sensor data corresponding to the pile. In this way we can simulate a situation of failure detection in the experiments.

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Authors and Affiliations

  1. Carnegie Mellon University in Qatar, Doha, Qatar

    Gianni A. Di Caro & Eduardo Feo-Flushing

Authors
  1. Gianni A. Di Caro
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  2. Eduardo Feo-Flushing
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Corresponding author

Correspondence to Gianni A. Di Caro .

Editor information

Editors and Affiliations

  1. Queen Mary University of London, London, UK

    Kaspar Althoefer

  2. Queen Mary University of London, London, UK

    Jelizaveta Konstantinova

  3. Queen Mary University of London, London, UK

    Ketao Zhang

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Di Caro, G.A., Feo-Flushing, E. (2019). Robot Path Planning Using Imprecise and Sporadic Advisory Information from Humans. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_21

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  • DOI: https://doi.org/10.1007/978-3-030-25332-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25331-8

  • Online ISBN: 978-3-030-25332-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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