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Scenario Reduction for Probabilistic Robot Path Planning in the Presence of Preferences on Hidden States

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Abstract

In practical robot motion planning, robots usually do not have a full model of their surrounding, and hence no complete and correct plan can be prepared in advance. In other words, in real-world scenarios a mobile robot operates in a partially known environment, meaning that there exists incomplete information about the true state of several ‘hidden’ variables, such as open/closed doors, which may represent potential blockages of the path of the robot. Consequently, a robot may have a probability distribution estimation of the states of the hidden variables and a preference over the possible values of such hidden variables. In this paper, to deal with the problem of choosing optimal policies for planning in off-line mode, a linear programming model is developed that incorporates the probability distribution of hidden variables. Furthermore, a heuristic method is proposed for planning in the presence of numerous hidden variables which relies on optimistic assumptive planning and produces near-optimal policies.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Jundi-Shapur University of Technology, 64615-334, Dezful, Iran

    Mohamad Ali Movafaghpour

  2. Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Jalal Al Ahmad, 14115-143, Tehran, Iran

    Mohamad Ali Movafaghpour & Ellips Masehian

  3. School of Electrical and Computer Engineering, University of Tehran, Campus #2, 14395-515, Tehran, Iran

    Hadi Moradi

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  1. Mohamad Ali Movafaghpour
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  2. Ellips Masehian
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  3. Hadi Moradi
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Correspondence to Ellips Masehian.

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Movafaghpour, M.A., Masehian, E. & Moradi, H. Scenario Reduction for Probabilistic Robot Path Planning in the Presence of Preferences on Hidden States. Arab J Sci Eng 39, 2909–2928 (2014). https://doi.org/10.1007/s13369-014-0959-0

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  • DOI: https://doi.org/10.1007/s13369-014-0959-0

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