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Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints

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Robotics Research (ISRR 2022)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 27))

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Abstract

Autonomous agents operating in perceptually aliased environments should ideally be able to solve the data association problem. Yet, planning for future actions while considering this problem is not trivial. State of the art approaches therefore use multi-modal hypotheses to represent the states of the agent and of the environment. However, explicitly considering all possible data associations, the number of hypotheses grows exponentially with the planning horizon. As such, the corresponding Belief Space Planning problem quickly becomes unsolvable. Moreover, under hard computational budget constraints, some non-negligible hypotheses must eventually be pruned in both planning and inference. Nevertheless, the two processes are generally treated separately and the effect of budget constraints in one process over the other was barely studied. We present a computationally efficient method to solve the nonmyopic Belief Space Planning problem while reasoning about data association. Moreover, we rigorously analyze the effects of budget constraints in both inference and planning.

This research was partially supported by US NSF/US-Israel BSF, and by Israel PBC-VATAT and the Technion Artificial Intelligence Hub (Tech.AI).

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Authors and Affiliations

  1. Technion - Israel Institute of Technology, 32000, Haifa, Israel

    Moshe Shienman & Vadim Indelman

Authors
  1. Moshe Shienman
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  2. Vadim Indelman
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Correspondence to Moshe Shienman .

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Editors and Affiliations

  1. EPFL STI SMT-GE, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, Switzerland

    Aude Billard

  2. Institute for Anthropomatics and Robotic, KIT, Karlsruhe, Baden-Württemberg, Germany

    Tamim Asfour

  3. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, Stanford, CA, USA

    Oussama Khatib

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Shienman, M., Indelman, V. (2023). Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints. In: Billard, A., Asfour, T., Khatib, O. (eds) Robotics Research. ISRR 2022. Springer Proceedings in Advanced Robotics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-031-25555-7_8

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