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Aggressive Moving Obstacle Avoidance Using a Stochastic Reachable Set Based Potential Field

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Book cover Algorithmic Foundations of Robotics XI

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 107))

Abstract

Identifying collision-free trajectories in environments with dynamic obstacles is a significant challenge. However, many pertinent problems occur in dynamic environments, e.g., flight coordination, satellite navigation, autonomous driving, and household robotics. Stochastic reachable (SR) sets assure collision-free trajectories with a certain likelihood in dynamic environments , but are infeasible for multiple moving obstacles as the computation scales exponentially in the number of Degrees of Freedom (DoF) of the relative robot-obstacle state space. Other methods, such as artificial potential fields (APF), roadmap-based methods, and tree-based techniques can scale well with the number of obstacles. However, these methods usually have low success rates in environments with a large number of obstacles. In this paper, we propose a method to integrate formal SR sets with ad-hoc APFs for multiple moving obstacles. The success rate of this method is 30 % higher than two related methods for moving obstacle avoidance, a roadmap-based technique that uses a SR bias and an APF technique without a SR bias, reaching over 86 % success in an enclosed space with 100 moving obstacles that ricochet off the walls.

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Acknowledgments

Chiang, Lesser, and Oishi are supported in part by National Science Foundation (NSF) Career Award CMMI-1254990 and NSF Award CPS-1329878. Tapia and Malone are supported in part by the National Institutes of Health (NIH) Grant P20GM110907 to the Center for Evolutionary and Theoretical Immunology.

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Authors and Affiliations

  1. Physics and Astronomy, University of New Mexico, 1919 Lomas Blvd NE, Albuquerque, NM, 87131, USA

    Hao-Tien Chiang

  2. Computer Science, University of New Mexico, MSC01 1130 1, Albuquerque, NM, 87131, USA

    Nick Malone & Lydia Tapia

  3. Electrical and Computer Engineering, University of New Mexico, MSC01 1100 1, Albuquerque, NM, 87131, USA

    Kendra Lesser & Meeko Oishi

Authors
  1. Hao-Tien Chiang
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  2. Nick Malone
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  3. Kendra Lesser
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  4. Meeko Oishi
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  5. Lydia Tapia
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Corresponding author

Correspondence to Lydia Tapia .

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Editors and Affiliations

  1. Department of Computer Engineering, Bogazici University, Istanbul, Turkey

    H. Levent Akin

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA

    Nancy M. Amato

  3. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA

    Volkan Isler

  4. Department of Information and Computing Sciences, Utrecht University, Utrecht, Utrecht, The Netherlands

    A. Frank van der Stappen

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Chiang, HT., Malone, N., Lesser, K., Oishi, M., Tapia, L. (2015). Aggressive Moving Obstacle Avoidance Using a Stochastic Reachable Set Based Potential Field. In: Akin, H., Amato, N., Isler, V., van der Stappen, A. (eds) Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-16595-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-16595-0_5

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